{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "import time\n",
    "import os\n",
    "\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load house records into dataframe\n",
    "data_column = ['Car_No', 'Car_Price',  'Car_ORC', 'Car_Title', 'Car_Year', 'Car_Brand', 'Car_Model','Car_Location','Car_Mileage(km)',  'Car_Type', 'Car_Gear', 'Car_Seat', 'Car_Fuel(cc)',  'Car_Door_Num',  'Car_Wof_Exp', 'Car_Fuel_Type', 'Car_Reg', 'Car_Color']\n",
    "Car_data_target = pd.read_csv('car_result_FINAL.txt', sep = '|', dtype = 'str', header=None)\n",
    "Car_data_target.columns = data_column \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13442, 18)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check on duplicate values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5533</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_No Car_Price Car_ORC                                 Car_Title  \\\n",
       "594   89038      9990       0                          2007 Lexus IS250   \n",
       "1342  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "5533  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "\n",
       "     Car_Year   Car_Brand Car_Model Car_Location Car_Mileage(km)   Car_Type  \\\n",
       "594      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "1342     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "5533     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "\n",
       "       Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Wof_Exp Car_Fuel_Type  \\\n",
       "594   Tiptronic      NaN         2500          NaN         NaN        Petrol   \n",
       "1342  Automatic        5         1389          NaN         NaN        Petrol   \n",
       "5533  Automatic        5         3498            5         NaN        Petrol   \n",
       "\n",
       "       Car_Reg Car_Color  \n",
       "594        NaN     White  \n",
       "1342  May 2021       Red  \n",
       "5533       NaN      Gold  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target.duplicated(['Car_No']) == True)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apr 2022</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mar 2022</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5532</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5533</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_No Car_Price Car_ORC                                 Car_Title  \\\n",
       "593   89038      9990       0                          2007 Lexus IS250   \n",
       "594   89038      9990       0                          2007 Lexus IS250   \n",
       "1341  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "1342  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "5532  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "5533  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "\n",
       "     Car_Year   Car_Brand Car_Model Car_Location Car_Mileage(km)   Car_Type  \\\n",
       "593      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "594      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "1341     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "1342     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "5532     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "5533     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "\n",
       "       Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Wof_Exp Car_Fuel_Type  \\\n",
       "593   Tiptronic      NaN         2500          NaN    Apr 2022        Petrol   \n",
       "594   Tiptronic      NaN         2500          NaN         NaN        Petrol   \n",
       "1341  Automatic        5         1389          NaN    Mar 2022        Petrol   \n",
       "1342  Automatic        5         1389          NaN         NaN        Petrol   \n",
       "5532  Automatic        5         3498            5         NaN        Petrol   \n",
       "5533  Automatic        5         3498            5         NaN        Petrol   \n",
       "\n",
       "       Car_Reg Car_Color  \n",
       "593        NaN     White  \n",
       "594        NaN     White  \n",
       "1341  May 2021       Red  \n",
       "1342  May 2021       Red  \n",
       "5532       NaN      Gold  \n",
       "5533       NaN      Gold  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target['Car_No'] == '89038') | (Car_data_target['Car_No'] == '85695') | (Car_data_target['Car_No'] == '75514')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop duplicate records "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target = Car_data_target.drop(Car_data_target[(Car_data_target['Car_No'] == '89038') & (Car_data_target['Car_Wof_Exp'].isnull() == True)].index)\n",
    "Car_data_target = Car_data_target.drop(Car_data_target[(Car_data_target['Car_No'] == '85695') & (Car_data_target['Car_Wof_Exp'].isnull() == True)].index)\n",
    "Car_data_target.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13439, 18)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Car_No, Car_Price, Car_ORC, Car_Title, Car_Year, Car_Brand, Car_Model, Car_Location, Car_Mileage(km), Car_Type, Car_Gear, Car_Seat, Car_Fuel(cc), Car_Door_Num, Car_Wof_Exp, Car_Fuel_Type, Car_Reg, Car_Color]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target.duplicated(['Car_No']) == True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_No                 0\n",
       "Car_Price            863\n",
       "Car_ORC                0\n",
       "Car_Title              0\n",
       "Car_Year               0\n",
       "Car_Brand              0\n",
       "Car_Model             91\n",
       "Car_Location           0\n",
       "Car_Mileage(km)        0\n",
       "Car_Type               1\n",
       "Car_Gear              33\n",
       "Car_Seat            2645\n",
       "Car_Fuel(cc)         725\n",
       "Car_Door_Num        2531\n",
       "Car_Wof_Exp        10634\n",
       "Car_Fuel_Type        496\n",
       "Car_Reg            12889\n",
       "Car_Color           1608\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing value percentage for each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Reg            0.959074\n",
       "Car_Wof_Exp        0.791279\n",
       "Car_Seat           0.196815\n",
       "Car_Door_Num       0.188332\n",
       "Car_Color          0.119652\n",
       "Car_Price          0.064216\n",
       "Car_Fuel(cc)       0.053947\n",
       "Car_Fuel_Type      0.036908\n",
       "Car_Model          0.006771\n",
       "Car_Gear           0.002456\n",
       "Car_Type           0.000074\n",
       "Car_Title          0.000000\n",
       "Car_ORC            0.000000\n",
       "Car_Mileage(km)    0.000000\n",
       "Car_Year           0.000000\n",
       "Car_Brand          0.000000\n",
       "Car_Location       0.000000\n",
       "Car_No             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target.isnull().sum()/len(Car_data_target))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop columns which missing percentage larger than 50%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop columns which missing percentage larger than 50% \n",
    "# drop Car_Reg and Wof_Exp \n",
    "Car_data_target.drop(['Car_Reg'], axis=1, inplace=True)\n",
    "\n",
    "Car_data_target.drop(['Car_Wof_Exp'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assign Car_Seat and Car_Door_Num according to Car_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "2        5\n",
       "6        4\n",
       "63       2\n",
       "122      3\n",
       "Name: Car_Door_Num, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Door_Num'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                5\n",
       "1                7\n",
       "4                8\n",
       "18             NaN\n",
       "31               4\n",
       "42               3\n",
       "46               2\n",
       "425              6\n",
       "1536    10 or more\n",
       "Name: Car_Seat, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Seat'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target['Car_Seat'] = Car_data_target['Car_Seat'].replace('10 or more', '10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Car_data_target[(Car_data_target['Car_Seat'] == '10 or more')]\n",
    "\n",
    "Car_data_target['Car_Seat'].fillna(value = 0, inplace=True)\n",
    "Car_data_target['Car_Door_Num'].fillna(value = 0, inplace=True)\n",
    "Car_data_target['Car_Fuel(cc)'].fillna(value = 0, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5\n",
       "1        7\n",
       "4        8\n",
       "18       0\n",
       "31       4\n",
       "42       3\n",
       "46       2\n",
       "425      6\n",
       "1536    10\n",
       "Name: Car_Seat, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Seat'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7</td>\n",
       "      <td>1800</td>\n",
       "      <td>0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>2400</td>\n",
       "      <td>5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>2400</td>\n",
       "      <td>5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72923</td>\n",
       "      <td>9900</td>\n",
       "      <td>1</td>\n",
       "      <td>2012 Nissan Serena 20X</td>\n",
       "      <td>2012</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Serena</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107126</td>\n",
       "      <td>Van</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8</td>\n",
       "      <td>2000</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Light Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No Car_Price Car_ORC                                          Car_Title  \\\n",
       "0  86261      3997       0                             2005 Nissan Tiida Axis   \n",
       "1  79395      7997       0                                   2009 Toyota Wish   \n",
       "2  85473     9940        1  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...   \n",
       "3  88342     8940        1  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...   \n",
       "4  72923     9900        1                             2012 Nissan Serena 20X   \n",
       "\n",
       "  Car_Year   Car_Brand  Car_Model Car_Location Car_Mileage(km)       Car_Type  \\\n",
       "0     2005      Nissan      Tiida      Dunedin          138150      Hatchback   \n",
       "1     2009      Toyota       Wish      Dunedin          100300  Station Wagon   \n",
       "2     2007  Mitsubishi  OUTLANDER     Auckland          102425         RV/SUV   \n",
       "3     2006  Mitsubishi  OUTLANDER     Auckland          109425         RV/SUV   \n",
       "4     2012      Nissan     Serena     Auckland          107126            Van   \n",
       "\n",
       "    Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Fuel_Type   Car_Color  \n",
       "0  Automatic        5         1500            0        Petrol      Silver  \n",
       "1  Automatic        7         1800            0        Petrol       White  \n",
       "2  Automatic        5         2400            5        Petrol      Silver  \n",
       "3  Automatic        5         2400            5        Petrol       Black  \n",
       "4  Automatic        8         2000            5           NaN  Light Blue  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tiida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>TIIDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4145</th>\n",
       "      <td>tiida</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_Model\n",
       "0        Tiida\n",
       "75       TIIDA\n",
       "4145     tiida"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[['Car_Model']][(Car_data_target['Car_Model'].str.upper() == 'TIIDA')].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert data type from object to int\n",
    "Car_data_target['Car_Seat'] = Car_data_target['Car_Seat'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Door_Num'] = Car_data_target['Car_Door_Num'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Fuel(cc)'] = Car_data_target['Car_Fuel(cc)'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Fuel(cc)'] = Car_data_target['Car_Fuel(cc)'].astype(int)\n",
    "\n",
    "\n",
    "Car_data_target['Car_Model_upper'] = Car_data_target['Car_Model'].str.upper()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Get median door number and seat for each brand and model\n",
    "\n",
    "Car_median_seat = Car_data_target[(Car_data_target['Car_Seat'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Seat']].median().reset_index()\n",
    "\n",
    "Car_median_door = Car_data_target[(Car_data_target['Car_Door_Num'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Door_Num']].median().reset_index()\n",
    "\n",
    "Car_median_fuel = Car_data_target[(Car_data_target['Car_Fuel(cc)'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "  \n",
    "# Convert to integer format\n",
    "    \n",
    "Car_median_seat['Car_Seat'] = Car_median_seat['Car_Seat'].astype(int)\n",
    "\n",
    "Car_median_door['Car_Door_Num'] = Car_median_door['Car_Door_Num'].astype(int)\n",
    "\n",
    "Car_median_fuel['Car_Fuel(cc)'] = Car_median_fuel['Car_Fuel(cc)'].astype(int)\n",
    " \n",
    "#,'Car_Seat', 'Car_Door_Num']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model_upper</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aston</td>\n",
       "      <td>MARTIN</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>4735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aston</td>\n",
       "      <td>MARTIN</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Audi</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>1.8T</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>1798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Brand Car_Model_upper       Car_Type  Car_Fuel(cc)\n",
       "0     Aston          MARTIN          Coupe          4735\n",
       "1     Aston          MARTIN          Sedan          6000\n",
       "2      Audi             1.8          Sedan          1800\n",
       "3      Audi            1.8T  Station Wagon          1798\n",
       "4      Audi             2.0          Coupe          1984"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_median_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1506 entries, 0 to 1505\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Car_Brand        1506 non-null   object\n",
      " 1   Car_Model_upper  1506 non-null   object\n",
      " 2   Car_Type         1506 non-null   object\n",
      " 3   Car_Door_Num     1506 non-null   int32 \n",
      "dtypes: int32(1), object(3)\n",
      "memory usage: 41.3+ KB\n"
     ]
    }
   ],
   "source": [
    "Car_median_door.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat = pd.merge(Car_data_target, Car_median_seat, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median seat number if seat number is null \n",
    "Car_data_target_seat.loc[Car_data_target_seat['Car_Seat_x'] == 0, 'Car_Seat_x'] = Car_data_target_seat.loc[Car_data_target_seat['Car_Seat_x'] == 0, 'Car_Seat_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18       5.0\n",
       "20       7.0\n",
       "33       7.0\n",
       "37       5.0\n",
       "44       NaN\n",
       "        ... \n",
       "13386    5.0\n",
       "13390    5.0\n",
       "13392    4.0\n",
       "13416    5.0\n",
       "13434    7.0\n",
       "Name: Car_Seat_x, Length: 2645, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat.loc[Car_data_target[(Car_data_target['Car_Seat'] == 0)].index, 'Car_Seat_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18       5.0\n",
       "20       7.0\n",
       "33       7.0\n",
       "37       5.0\n",
       "44       NaN\n",
       "        ... \n",
       "13386    5.0\n",
       "13390    5.0\n",
       "13392    4.0\n",
       "13416    5.0\n",
       "13434    7.0\n",
       "Name: Car_Seat_y, Length: 2645, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat.loc[Car_data_target[(Car_data_target['Car_Seat'] == 0)].index, 'Car_Seat_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door = pd.merge(Car_data_target_seat, Car_median_door, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median door number if seat number is null \n",
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_x'] = Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13439, 19)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.0\n",
       "1        5.0\n",
       "20       5.0\n",
       "33       5.0\n",
       "35       4.0\n",
       "        ... \n",
       "13400    2.0\n",
       "13406    4.0\n",
       "13413    5.0\n",
       "13416    5.0\n",
       "13434    5.0\n",
       "Name: Car_Door_Num_x, Length: 2531, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.0\n",
       "1        5.0\n",
       "20       5.0\n",
       "33       5.0\n",
       "35       4.0\n",
       "        ... \n",
       "13400    2.0\n",
       "13406    4.0\n",
       "13413    5.0\n",
       "13416    5.0\n",
       "13434    5.0\n",
       "Name: Car_Door_Num_y, Length: 2531, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel = pd.merge(Car_data_target_seat_door, Car_median_fuel, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median door number if seat number is null \n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_x'] = Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44          NaN\n",
       "127         NaN\n",
       "138      2350.0\n",
       "167      1240.0\n",
       "218      2400.0\n",
       "          ...  \n",
       "13371    1500.0\n",
       "13372    1800.0\n",
       "13374    1500.0\n",
       "13390    3498.0\n",
       "13396    2000.0\n",
       "Name: Car_Fuel(cc)_x, Length: 725, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44          NaN\n",
       "127         NaN\n",
       "138      2350.0\n",
       "167      1240.0\n",
       "218      2400.0\n",
       "          ...  \n",
       "13371    1500.0\n",
       "13372    1800.0\n",
       "13374    1500.0\n",
       "13390    3498.0\n",
       "13396    2000.0\n",
       "Name: Car_Fuel(cc)_y, Length: 725, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_columns = ['Car_No', 'Car_Price', 'Car_ORC', 'Car_Title', 'Car_Year', 'Car_Brand',\n",
    "       'Car_Model', 'Car_Location', 'Car_Mileage(km)', 'Car_Type', 'Car_Gear',\n",
    "       'Car_Seat', 'Car_Fuel(cc)', 'Car_Door_Num', 'Car_Fuel_Type',\n",
    "       'Car_Color']\n",
    "Car_data_target_seat_door_fuel.drop(['Car_Seat_y', 'Car_Door_Num_y', 'Car_Model_upper', 'Car_Fuel(cc)_y'], axis=1, inplace=True) \n",
    "Car_data_target_seat_door_fuel.columns = data_columns \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72923</td>\n",
       "      <td>9900</td>\n",
       "      <td>1</td>\n",
       "      <td>2012 Nissan Serena 20X</td>\n",
       "      <td>2012</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Serena</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107126</td>\n",
       "      <td>Van</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Light Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No Car_Price Car_ORC                                          Car_Title  \\\n",
       "0  86261      3997       0                             2005 Nissan Tiida Axis   \n",
       "1  79395      7997       0                                   2009 Toyota Wish   \n",
       "2  85473     9940        1  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...   \n",
       "3  88342     8940        1  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...   \n",
       "4  72923     9900        1                             2012 Nissan Serena 20X   \n",
       "\n",
       "  Car_Year   Car_Brand  Car_Model Car_Location Car_Mileage(km)       Car_Type  \\\n",
       "0     2005      Nissan      Tiida      Dunedin          138150      Hatchback   \n",
       "1     2009      Toyota       Wish      Dunedin          100300  Station Wagon   \n",
       "2     2007  Mitsubishi  OUTLANDER     Auckland          102425         RV/SUV   \n",
       "3     2006  Mitsubishi  OUTLANDER     Auckland          109425         RV/SUV   \n",
       "4     2012      Nissan     Serena     Auckland          107126            Van   \n",
       "\n",
       "    Car_Gear  Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type   Car_Color  \n",
       "0  Automatic       5.0        1500.0           5.0        Petrol      Silver  \n",
       "1  Automatic       7.0        1800.0           5.0        Petrol       White  \n",
       "2  Automatic       5.0        2400.0           5.0        Petrol      Silver  \n",
       "3  Automatic       5.0        2400.0           5.0        Petrol       Black  \n",
       "4  Automatic       8.0        2000.0           5.0           NaN  Light Blue  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>1990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2455</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2708</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3804</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4265</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4448</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4562</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9542</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11500</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12776</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Car_Brand  Car_Model       Car_Type  Car_Fuel(cc)\n",
       "2      Mitsubishi  OUTLANDER         RV/SUV        2400.0\n",
       "64     Mitsubishi  Outlander         RV/SUV        2400.0\n",
       "85     Mitsubishi  Outlander         RV/SUV        2359.0\n",
       "175    Mitsubishi  Outlander  Station Wagon        2359.0\n",
       "273    Mitsubishi  Outlander  Station Wagon        2997.0\n",
       "331    Mitsubishi  Outlander         RV/SUV        2352.0\n",
       "592    Mitsubishi  Outlander         RV/SUV        1990.0\n",
       "723    Mitsubishi  OUTLANDER  Station Wagon        2400.0\n",
       "1049   Mitsubishi  Outlander         RV/SUV           0.0\n",
       "1147   Mitsubishi  Outlander         RV/SUV        2000.0\n",
       "1671   Mitsubishi  Outlander  Station Wagon        1998.0\n",
       "2156   Mitsubishi  Outlander         RV/SUV        2300.0\n",
       "2455   Mitsubishi  Outlander  Station Wagon           0.0\n",
       "2708   Mitsubishi  Outlander         RV/SUV        2350.0\n",
       "3804   Mitsubishi  Outlander         RV/SUV        2360.0\n",
       "4265   Mitsubishi  Outlander  Station Wagon        2400.0\n",
       "4448   Mitsubishi  Outlander  Station Wagon        2379.0\n",
       "4562   Mitsubishi  Outlander  Station Wagon        2350.0\n",
       "9542   Mitsubishi  OUTLANDER         RV/SUV        2350.0\n",
       "11500  Mitsubishi  OUTLANDER         RV/SUV        2356.0\n",
       "12776  Mitsubishi  OUTLANDER         RV/SUV           0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Car_data_target_seat_door.groupby(['Car_Brand', 'Car_Model', 'Car_Type'])[['Car_Fuel(cc)']]\n",
    "\n",
    "Car_data_target_seat_door_fuel[['Car_Brand', 'Car_Model', 'Car_Type', 'Car_Fuel(cc)']][(Car_data_target_seat_door['Car_Model'].str.upper() == 'OUTLANDER')].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13439 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           13439 non-null  object \n",
      " 1   Car_Price        12576 non-null  object \n",
      " 2   Car_ORC          13439 non-null  object \n",
      " 3   Car_Title        13439 non-null  object \n",
      " 4   Car_Year         13439 non-null  object \n",
      " 5   Car_Brand        13439 non-null  object \n",
      " 6   Car_Model        13348 non-null  object \n",
      " 7   Car_Location     13439 non-null  object \n",
      " 8   Car_Mileage(km)  13439 non-null  object \n",
      " 9   Car_Type         13438 non-null  object \n",
      " 10  Car_Gear         13406 non-null  object \n",
      " 11  Car_Seat         13159 non-null  float64\n",
      " 12  Car_Fuel(cc)     13410 non-null  float64\n",
      " 13  Car_Door_Num     13351 non-null  float64\n",
      " 14  Car_Fuel_Type    12943 non-null  object \n",
      " 15  Car_Color        11831 non-null  object \n",
      "dtypes: float64(3), object(13)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.119652\n",
       "Car_Price          0.064216\n",
       "Car_Fuel_Type      0.036908\n",
       "Car_Seat           0.020835\n",
       "Car_Model          0.006771\n",
       "Car_Door_Num       0.006548\n",
       "Car_Gear           0.002456\n",
       "Car_Fuel(cc)       0.002158\n",
       "Car_Type           0.000074\n",
       "Car_Mileage(km)    0.000000\n",
       "Car_Location       0.000000\n",
       "Car_Brand          0.000000\n",
       "Car_Year           0.000000\n",
       "Car_Title          0.000000\n",
       "Car_ORC            0.000000\n",
       "Car_No             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop rows with null price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.0\n",
       "Car_Fuel_Type      0.0\n",
       "Car_Door_Num       0.0\n",
       "Car_Fuel(cc)       0.0\n",
       "Car_Seat           0.0\n",
       "Car_Gear           0.0\n",
       "Car_Type           0.0\n",
       "Car_Mileage(km)    0.0\n",
       "Car_Location       0.0\n",
       "Car_Model          0.0\n",
       "Car_Brand          0.0\n",
       "Car_Year           0.0\n",
       "Car_Title          0.0\n",
       "Car_ORC            0.0\n",
       "Car_Price          0.0\n",
       "Car_No             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10360, 16)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete records with P.O.A price\n",
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Price'] == 'P.0.A')].index, axis=0, inplace = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.0\n",
       "Car_Fuel_Type      0.0\n",
       "Car_Door_Num       0.0\n",
       "Car_Fuel(cc)       0.0\n",
       "Car_Seat           0.0\n",
       "Car_Gear           0.0\n",
       "Car_Type           0.0\n",
       "Car_Mileage(km)    0.0\n",
       "Car_Location       0.0\n",
       "Car_Model          0.0\n",
       "Car_Brand          0.0\n",
       "Car_Year           0.0\n",
       "Car_Title          0.0\n",
       "Car_ORC            0.0\n",
       "Car_Price          0.0\n",
       "Car_No             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to integer for numeric columns \n",
    "Car_data_target_seat_door_fuel['Car_Price'] = Car_data_target_seat_door_fuel['Car_Price'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Year'] = Car_data_target_seat_door_fuel['Car_Year'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Mileage(km)'] = Car_data_target_seat_door_fuel['Car_Mileage(km)'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Fuel(cc)'] = Car_data_target_seat_door_fuel['Car_Fuel(cc)'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Price'] = Car_data_target_seat_door_fuel['Car_Price'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_ORC'] = Car_data_target_seat_door_fuel['Car_ORC'].astype('int')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10069, 16)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10069 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           10069 non-null  object \n",
      " 1   Car_Price        10069 non-null  int32  \n",
      " 2   Car_ORC          10069 non-null  int32  \n",
      " 3   Car_Title        10069 non-null  object \n",
      " 4   Car_Year         10069 non-null  int32  \n",
      " 5   Car_Brand        10069 non-null  object \n",
      " 6   Car_Model        10069 non-null  object \n",
      " 7   Car_Location     10069 non-null  object \n",
      " 8   Car_Mileage(km)  10069 non-null  int32  \n",
      " 9   Car_Type         10069 non-null  object \n",
      " 10  Car_Gear         10069 non-null  object \n",
      " 11  Car_Seat         10069 non-null  float64\n",
      " 12  Car_Fuel(cc)     10069 non-null  int32  \n",
      " 13  Car_Door_Num     10069 non-null  float64\n",
      " 14  Car_Fuel_Type    10069 non-null  object \n",
      " 15  Car_Color        10069 non-null  object \n",
      "dtypes: float64(2), int32(5), object(9)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\env\\ad\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Fuel(cc)')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Fuel(cc)'])\n",
    "plt.title('Distribution of Car Fuel(cc)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete Outlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>76494</td>\n",
       "      <td>6950</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Honda Odyssey</td>\n",
       "      <td>2006</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Odyssey</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>111889</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>111889</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4006</th>\n",
       "      <td>76004</td>\n",
       "      <td>17495</td>\n",
       "      <td>1</td>\n",
       "      <td>2011 Audi Cabriolet</td>\n",
       "      <td>2011</td>\n",
       "      <td>Audi</td>\n",
       "      <td>Cabriolet</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>57325</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Grey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10963</th>\n",
       "      <td>31643</td>\n",
       "      <td>9440</td>\n",
       "      <td>1</td>\n",
       "      <td>2008 Toyota Auris</td>\n",
       "      <td>2008</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Auris</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>98000</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>98000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12884</th>\n",
       "      <td>23440</td>\n",
       "      <td>12990</td>\n",
       "      <td>1</td>\n",
       "      <td>2008 Toyota Harrier</td>\n",
       "      <td>2008</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Harrier</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>97500</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Car_No  Car_Price  Car_ORC            Car_Title  Car_Year Car_Brand  \\\n",
       "3999   76494       6950        1   2006 Honda Odyssey      2006     Honda   \n",
       "4006   76004      17495        1  2011 Audi Cabriolet      2011      Audi   \n",
       "10963  31643       9440        1    2008 Toyota Auris      2008    Toyota   \n",
       "12884  23440      12990        1  2008 Toyota Harrier      2008    Toyota   \n",
       "\n",
       "       Car_Model Car_Location  Car_Mileage(km)       Car_Type   Car_Gear  \\\n",
       "3999     Odyssey     Auckland           111889  Station Wagon  Automatic   \n",
       "4006   Cabriolet     Auckland            57325    Convertible  Tiptronic   \n",
       "10963      Auris     Auckland            98000      Hatchback  Automatic   \n",
       "12884    Harrier     Auckland            97500         RV/SUV  Automatic   \n",
       "\n",
       "       Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type Car_Color  \n",
       "3999        7.0        111889           5.0        Petrol     White  \n",
       "4006        4.0         20000           2.0        Petrol      Grey  \n",
       "10963       5.0         98000           5.0        Petrol       Red  \n",
       "12884       5.0         23600           4.0        Petrol     Black  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Fuel(cc)'] > 10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete 76004\n",
    "\n",
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '76004')].index, axis=0, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Odyssey = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Odyssey') & (Car_data_target_seat_door_fuel['Car_Type'] == 'Station Wagon') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Honda')].groupby(['Car_Model', 'Car_Type', 'Car_Brand'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '76494')].index, 'Car_Fuel(cc)'] = median_Odyssey.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Auris = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Auris') & (Car_data_target_seat_door_fuel['Car_Type'] == 'Hatchback') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Toyota')].groupby(['Car_Model', 'Car_Type'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '31643')].index, 'Car_Fuel(cc)'] = median_Auris.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Harrier = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Harrier') & (Car_data_target_seat_door_fuel['Car_Type'] == 'RV/SUV') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Toyota')].groupby(['Car_Model', 'Car_Type', 'Car_Brand'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '23440')].index, 'Car_Fuel(cc)'] = median_Harrier.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\env\\ad\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Fuel(cc)')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Fuel(cc)'])\n",
    "plt.title('Distribution of Car Fuel(cc)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\env\\ad\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Mileage(km)')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Mileage(km)'])\n",
    "plt.title('Distribution of Car Mileage(km)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Seat\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different Number of Seats', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Seat', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71719</td>\n",
       "      <td>9400</td>\n",
       "      <td>1</td>\n",
       "      <td>2010 Nissan X-Trail 20XT</td>\n",
       "      <td>2010</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>X-Trail</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>125384</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No  Car_Price  Car_ORC  \\\n",
       "0  86261       3997        0   \n",
       "1  79395       7997        0   \n",
       "2  85473       9940        1   \n",
       "3  88342       8940        1   \n",
       "5  71719       9400        1   \n",
       "\n",
       "                                           Car_Title  Car_Year   Car_Brand  \\\n",
       "0                             2005 Nissan Tiida Axis      2005      Nissan   \n",
       "1                                   2009 Toyota Wish      2009      Toyota   \n",
       "2  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...      2007  Mitsubishi   \n",
       "3  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...      2006  Mitsubishi   \n",
       "5                           2010 Nissan X-Trail 20XT      2010      Nissan   \n",
       "\n",
       "   Car_Model Car_Location  Car_Mileage(km)       Car_Type   Car_Gear  \\\n",
       "0      Tiida      Dunedin           138150      Hatchback  Automatic   \n",
       "1       Wish      Dunedin           100300  Station Wagon  Automatic   \n",
       "2  OUTLANDER     Auckland           102425         RV/SUV  Automatic   \n",
       "3  OUTLANDER     Auckland           109425         RV/SUV  Automatic   \n",
       "5    X-Trail     Auckland           125384         RV/SUV  Automatic   \n",
       "\n",
       "   Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type Car_Color  \n",
       "0       5.0          1500           5.0        Petrol    Silver  \n",
       "1       7.0          1800           5.0        Petrol     White  \n",
       "2       5.0          2400           5.0        Petrol    Silver  \n",
       "3       5.0          2400           5.0        Petrol     Black  \n",
       "5       5.0          2000           5.0        Petrol     Black  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Year\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different year of car', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Year', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete outlier: Car year earlier than 1985. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Year'] < 1990)].index, axis=0, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Year\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different year of car', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Year', fontsize = 15) \n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#box plot Districts/Total Price\n",
    "f, ax = plt.subplots(figsize=(15, 10))\n",
    "fig = sns.boxplot(x='Car_Type', y=\"Car_Price\", data=Car_data_target_seat_door_fuel)\n",
    "plt.title('Car Price for Different Car Type', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Type', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EuCrmDKlUeTgcjmOUjRs38vjjj/P000/z1FNP0dvbO9pFcgySERdq9nTWNwK/tdcCnA/81Qa5HbjC/l5tr7H+F9jwq4E7VbVHVXdjzkg6zX6qVHWXqvYCdwKr+8nD4XAcgzQ1NbFv376+69bWVvbs2TN6BXIMCaMxUvsx8F+YAxvBHJTXbE/vBXOWUuSk2unAfug73bfFhu9zT4iTyj1dHnGIyLUisk5E1tXV1R3hLTocjrFOd3fyMWZdXV0eIR1HEyMq1ETkcqBWVdePZL4DQVVvVtWVqrqysnJUrLw4HI4RoKKiguzs7Di3adOmjVJpHEPFSJvJOht4s4hchjklugT4f0CZiGTZkdQMzMm62O+ZQLWIZGGOLW+IcY8QG8fLvSFNHg6H4xgkOzubs846i507dxIIBJg1axauI3v0M6IjNVX9kqrOUNU5GEWPx1T1PcDjmKPYAa4B7rG/77XXWP/H1Kgo3QtcabUj5wILgReBtcBCq+mYY/O418ZJlYfD4ThGKS4uZsWKFZx66qlMnjx5tIvjGALGyj6164HPiUgVZv3rFut+CzDRun8O+CKAqm4B7gJeBf4FXKeqITsK+ySwBqNdeZcNmy4Ph8PhcIwTxO3NSM3KlSvVWel3OByOgSEi61V15WjkPVZGag6Hw+FwDBon1BwOh8MxbnBCzeFwOBzjBifUHA6HwzFucELN4XA4HOMGJ9QcDofDMW5wQs3hcDgc4wYn1BwOh8MxbnBCzeFwOBzjBifUHA6HwzFucELN4XA4HOMGJ9QcDofDMW4Y6fPUHA6HY8zQ09PDpk2baGhooKysjGXLllFQUDDaxXIMAjdSczgcxyybNm2ipqaGQCBAXV0dL7/88mgXyTFInFBzOBzHLA0NDXHXTU1NhEKhUSqNYyhwQs3hcByzlJaWxl0XFxfj9/tHqTSOoWBEhZqI5InIiyLyiohsEZGvW/fbRGS3iGywnxXWXUTkJyJSJSIbReTkmLSuEZEd9nNNjPspIrLJxvmJiIh1LxeRh234h0Vkwkjeu8PhGHssW7asT7AVFRWxYsWK0S2QY9CMtKJID3C+qraLSDbwtIg8aP2+oKp/TQh/KbDQfk4HfgmcLiLlwA3ASkCB9SJyr6o22TAfAV4AHgAuAR4Evgg8qqrfE5Ev2uvrh/FeHQ7HGKewsJBzzz2XUCjkRmjjhBEdqamh3V5m24+mibIauMPGex4oE5GpwMXAw6raaAXZw8Al1q9EVZ9XVQXuAK6ISet2+/v2GHeHw3GM4wTa+GHE19RExC8iG4BajGB6wXp9204x3iQiudZtOrA/Jnq1dUvnXu3hDjBZVQ/Z3zXA5BTlu1ZE1onIurq6uiO6R4fD4XCMDiMu1FQ1pKorgBnAaSKyFPgSsBg4FShnmKcF7SjOc4Soqjer6kpVXVlZWTmcxXA4HA7HEDNq2o+q2gw8DlyiqofsFGMP8H/AaTbYAWBmTLQZ1i2d+wwPd4DDdnoS+107pDfkcDgcjlFnpLUfK0WkzP7OB94AvBYjbASz1rXZRrkXuNpqQZ4BtNgpxDXARSIywWoxXgSssX6tInKGTetq4J6YtCJaktfEuDscDofjCNHmOkJ//j6hH19L6M/fR5tHd9lmpLUfpwK3i4gfI1DvUtX7ROQxEakEBNgAfMyGfwC4DKgCOoEPAKhqo4h8E1hrw31DVRvt708AtwH5GK3HiHbl94C7RORDwF7gncN1kw6Hw3GsEF5zKxzYbi4ObDfXo8iICjVV3Qic5OF+forwClyXwu9WIOnpqeo6YKmHewNwwQCL7HA4HI50HNqZ/nqEcRZFHA6Hw3HkTJ2f/nqEcULN4XA4HEeM7+IPwvRF4PPD9EXmehRxR884HA6H44iRskr87xo7xpncSM3hcDgc4wYn1BwOh8MxbnBCzeFwOBzjBifUHA6HwzFucELN4XA4HOMGJ9QyIKxhdrfW09jdMdpFcTgcDkcanEp/PzT1dHLTpkc53NWGAJfMPIEr5iwf7WI5HA6HwwM3UuuHNdWvcrirDTBn1fxr/xbqutrTR3I4HA7HqOCEWj809XTGXSvQ1OOmIR0Oh2Ms4oRaP6ysnB13XZ5bwPwSd3iow+FwjEXcmlo/nFo5G1Xlhdo9lOXmc+nME/D7XF/A4XA4xiJOqGXAaZPmcNqkOaNdDIfD4XD0gxtyOBwOh2PcMKJCTUTyRORFEXlFRLaIyNet+1wReUFEqkTkzyKSY91z7XWV9Z8Tk9aXrPs2Ebk4xv0S61YlIl+McffMw+FwOBzjh5EeqfUA56vqcmAFcImInAF8H7hJVRcATcCHbPgPAU3W/SYbDhFZAlwJnABcAvxCRPwi4gd+DlwKLAGusmFJk4fD4XA4xgkjKtTUENnklW0/CpwP/NW63w5cYX+vttdY/wtERKz7narao6q7gSrgNPupUtVdqtoL3AmstnFS5eFwOByOccKIr6nZEdUGoBZ4GNgJNKtq0AapBqbb39OB/QDWvwWYGOueECeV+8Q0eSSW71oRWSci6+rq6gZxpw6Hw+EYaUZcqKlqSFVXADMwI6vFI12GdKjqzaq6UlVXVla6/WgOh8NxNDFq2o+q2gw8DpwJlIlIZHvBDOCA/X0AmAlg/UuBhlj3hDip3BvS5OFwOI5RGhoa2L9/P93d3aNdFMcQMdLaj5UiUmZ/5wNvALZihNvbbbBrgHvs73vtNdb/MVVV636l1Y6cCywEXgTWAgutpmMORpnkXhsnVR4Oh+MYZNOmTTz33HO88sorPP744zQ1NY12kRxDwEiP1KYCj4vIRowAelhV7wOuBz4nIlWY9a9bbPhbgInW/XPAFwFUdQtwF/Aq8C/gOjutGQQ+CazBCMu7bFjS5OFwOI4xurq62Lt3b991KBSiqqpqFEvkGCpG1KKIqm4ETvJw34VZX0t07wbekSKtbwPf9nB/AHgg0zwcDsexRygUysjNcfThLIo4HI5jjqKiIioqKuLcZs+enSK042jC2X50pKUz2Msfq9aypekQMwrLuGr+qUwrLB3tYjkcg+bUU09l3759dHR0MHXqVCZOnDjaRXIMAW6k5kjLX3a9xNq6vXQGe9neUsuvtz6F0btxOI5u/H4/c+fOZenSpU6gjSOcUHOkZXvL4bjrmq5WWgNO/dnhcIxNnFBzpGV2UXwPtjy3gOLsvFEqjcPhcKTHCTVHWt4572QWlU4CYHJ+CR9efDY+kVEulcPhcHjjFEUcaSnLLeDzJ15IIBwi2+cf7eI4HA5HWtxIzZERTqA5HI6jASfUHA6HwzFucELN4XA4HOMGJ9QcDodjEGhvN6rh0S6Gw+IURRwOh+MI0M42wg/8GvZtheJyfBdejcxdNtrFOubJeKRmj3n5uIjcIiIPichC6/4uETl++IrocDgcYw99+m9GoAG0NRJ+8DdoMDC6hXJkJtREZBGwHfguMAe4ACi23ucCXxqOwjkcDsdYRWv3xTt0d0BL3egUxtFHpiO1nwD7MALtYiB29+2TwDlDWyyHw+EY28jMxfEOxeUwYcroFMbRR6ZC7Vzgu6raDCRasz2MOfyzX0Rkpog8LiKvisgWEfmMdb9RRA6IyAb7uSwmzpdEpEpEtonIxTHul1i3KhH5Yoz7XBF5wbr/2Z6AHZk+/bN1f0FE5mR47w6Hw5GEnHUFcuLrobAUpi/Ct/pTiM/p3o02mSqKdAP5KfymA80ZphMEPq+qL4lIMbBeRB62fjep6v/GBhaRJcCVwAnANOAROxUK8HPgDUA1sFZE7lXVV4Hv27TuFJFfAR8Cfmm/m1R1gYhcacO9K8NyOxwORxySnYNceDVcePVoF8URQ6bdioeBL4tI7EFaKiK5wKfwOGnaC1U9pKov2d9twFaMUEzFauBOVe1R1d1AFeb06tOAKlXdpaq9wJ3AahER4Hzgrzb+7cAVMWndbn//FbjAhnc4HA7HOCFTofYFoBIjVH6HmYL8GrAJM4L6ykAzttN/JwEvWKdPishGEblVRCZYt+nA/pho1dYtlftEoFlVgwnucWlZ/xYbPrFc14rIOhFZV1fnFn0dDofjaCIjoaaq+4HlwK8wyiI7MetofwFOUdWagWQqIkXA34DPqmorZnpwPrACOAT8cCDpDSWqerOqrlTVlZWVlaNVDIfD4XAcARlvvlbVJuCr9nPEiEg2RqD9QVX/btM+HOP/G+A+e3kAmBkTfYZ1I4V7A1AmIll2NBYbPpJWtYhkAaU2vMPhcDjGCZnuU1seq5GY4HeZiJyYYToC3AJsVdUfxbjHak++Bdhsf98LXGk1F+cCC4EXgbXAQqvpmINRJrlXVRV4HHi7jX8NcE9MWtfY328HHrPhHQ6HwzFOyHSkdhPwFN4KIacCn8dsyO6Ps4H3AZtEZIN1+zJwlYiswKzV7QE+CqCqW0TkLuBVjObkdaoaAhCRTwJrAD9wq6puseldD9wpIt8CXsYIUez370SkCmjECEKHw+FwjCMkk8GKiDQD71TVhzz8LsJoKJYPffFGl5UrV+q6detGuxgOh8NxVCEi61V15Wjknan2ox8oTOFXCOQMTXEcDofD4ThyMhVqa4FrU/hdC7jhjMPhOOpQVerq6qipqSEUCo12cRxDQKZrajdirHm8gNnAXINR6b8ao+r/hmEpncPhcAwT3d3dPPXUU/T09ADg8/k488wzmTBhQj8xHWOZTPep/Ru4CAgDP8VY5Ph/GOWNN6jqU8NWQofD4RgGdu7c2SfQAMLhMJs3b04Tw3E0MJB9ak8AZ4pIATABY0exc7gK5nA4HMNJR0dHkltXV9colMQxlAzYpLSqdqrqASfQHA7H0cyMGTMycnMcXaQcqYnID4CfqGq1/Z0OVdXrh7ZoDofDMXxMmzaNQCBAVVUV4XCY6dOns3jx4v4jOsY06aYf3wH8AWMU+J0kn6MWi2I2PTscDsdRw+zZs5k9e/ZoF8MxhKQUaqo6N+b3nBEpjcPhcDgcg6DfNTURyRORh0Rk1fAXx+FwOByOI6dfoaaq3Rj7jv7hL47D4XA4HEdOptqP9xI9QdrhcDgcjjFJpvvU1gD/Y4+IeQA4TILiiKp6WfB3OBwOh2PEyFSo/d5+v9V+ElHc9KTD4XA4RplMhdrc/oM4HA6HwzG69CvURGQiUAHUqOqB4S+Sw+FwOBxHRkpFEREptqdO1wIvAvtE5HkRWXCkmYnITBF5XEReFZEtIvIZ614uIg+LyA77PcG6i4j8RESqRGSjiJwck9Y1NvwOEbkmxv0UEdlk4/xERCRdHo7Bo8EAun0d4a3Po4Ge/iM4HA7HMJFO+/HrwKXA14A3Ap8EpgO3DCK/IPB5VV0CnAFcJyJLgC8Cj6rqQuBRe43Nf6H9XAv8EoyAAm4ATgdOA26IEVK/BD4SE+8S654qD8cg0EAv4T99m/B9v0Qf/A3hO25Au9pHu1gOh+MYJZ1QezPw36r6bVX9l6r+Eng3cI6IlB5JZqp6SFVfsr/bgK0YQbkac04b9vsK+3s1cIcangfKrAbmxcDDqtqoqk3Aw8Al1q9EVZ9XVQXuSEjLKw/HINCql6Buf9ShpQ599ZnRK5DD4TimSSfUZmNOvI7lBUCs36AQkTnASTbNyap6yHrVAJPt7+lATItJtXVL517t4U6aPBLLda2IrBORdXV1dUdwZ8cYwd5kt4CHm8PhcIwA6YSaHwgkuIVi/I4YESkC/gZ8VlVbY/3sCCud8eRBky4PVb1ZVVeq6srKysrhLMa4QBacDIUxA/fcfGTJmaNXIIfDcUzTn/bjd0WkMeZa7PcPRKQpxl1V9V2ZZCgi2RiB9gdV/bt1PiwiU1X1kJ1CrLXuB4CZMdFnWLcDwKoE9yes+wyP8OnycGTA1qYa9nc0sbhsMrOKyvvcJb8I33u+im5+GkJB5IRzkJKKUSypw+E4lkkn1P6NGZElDleetPEGPIyxmoi3AFtV9UcxXvcC1wDfs9/3xLh/UkTuxCiFtFihtAb4ToxyyEXAl1S1UURaReQMzLTm1cBP+8nD0Q//2PMKD+7fAphezfuPO5MzJkW3LkrRBOSMN41S6RwOhyNKuqNnVg1DfmcD7wM2icgG6/ZljKC5S0Q+BOzFnN8GxiTXZUAV0Al8wJatUUS+SXTN7xuqGhlRfgK4DcgHHrQf0uThSEMgHOKRA6/1XSvwr/2vxgk1h8PhGCtkalFkSFDVp4lOYSZygUd4Ba5LkdatwK0e7uuApR7uDV55ONKjqpi/IUpYh3XJ0+FwOI6YTK30O45RcvxZvH7qwji3N0x3R947HI6xyYiO1BxHH2FVKvIKmVpQSq4/izfPWsYJ5dNGu1gOh8PhiRupOdLy29ee5s+7XuJQZwt72hq4Y8eLhMLh0S6Ww+FweOKEmiMlXcFeXqrfH+fW3NvJ1uaaUSqRw+FwpKdfoSYieSLSIyJXjEB5HGMIQfDS6/FJKl0fh8PhGF36FWqq2o3ZqBwc/uI4xhJ5WdmcNTledX9SXhGLyzwtjDkcDseok6miyK+BT4vIGlVNNJ3lGCTd2+rp3d9C9tRi8pZUImNoJHT1ojNYMmEqa+v2Mrd4IhdMX4xP3Ky1wwGgh/eirz0PBSXIstcheYWjXaRjnkyFWhlm79ceEXkUOEy87URV1euHuGzHBO3P7aP96X3m4uVDBA63U3L+vNEtVAIrK2ezsnLQNqwdjnGFHqwifNcPIGxM4uqrz+F7342Iz3X6RpNMhdrbgMjpj+d6+CvghNoR0PnyofjrV2ooXjUX8Y2d0ZrD4UhGNz7ZJ9AAaDgA1dthltvHOZpkJNRU1dlEGiYkO/4wBMnypba54nA4xg7ZuR5uOSNfDkccbpw8yhSdPStOiBWdPWtMrak5HA5v5OQ3QF5R1GHecmTq2Fo6OBYZkEURETkHWATkJfqp6i+GqlDHEvlLJpE9pZje6haypxSTPWlsLTQf6mzhnj2vUNPVyoKSSt48+0RKcvJHu1gOx6gjEybj+8C30d0bkYISmL1ktIvkIEOhJiKTgUeBJZj1s8hQIlZZxAm1IySrPJ+s8rEnKBq7O/j2yw8SsBZEDnW28mpTDd9c+Sb8bjHc4UDyi5AlZ412MRwxZNoy/RBowRzYKZizzeYAXwV2YEZvjnHG2vq9fQItQkNPB9tb3PmqDodjbJKpUHs9RrBFVPVEVfep6neA3+NGaWhvF1p/AB1HdhELszwWwoFCtxjucDjGKAPZp1anqmERaQUmxfg9yzGuzh/e+jz6yB0Q6IHSSnxv+SxSPmW0izVoTquczaMHXuNgZ0uf2ykVs5hVVD6KpXI4HI7UZDpS2w1Mtb+3AO+J8XsT0JgUwwMRuVVEakVkc4zbjSJyQEQ22M9lMX5fEpEqEdkmIhfHuF9i3apE5Isx7nNF5AXr/mcRybHuufa6yvrPyfC++0UDvehjvzcCDaCljvBTfx2q5EeVHH8WXz35Uj6x5HVcPmspXzjxQq49/pzRLpbD4XCkJFOhdj9wkf39LeBtIlItIruBTwM/zTCd24BLPNxvUtUV9vMAgIgsAa4ETrBxfiEifhHxAz8HLsUorlxlwwJ836a1AGgCPmTdPwQ0WfebbLihoasNerri3ZoPD1nyo41PfCyfOIM3zT6RBaWT+o/gcDgco0hGQk1Vv6SqH7a/HwTOAm4H7gYuV9X/zTCdf5PhqA5YDdypqj2quhuoAk6znypV3aWqvcCdwGoxm7vOByLDpNuBK2LSut3+/itwgQzRZjApmQiT4k1IyYKThyJph8PhcAyQIzr5WlXXAeuGsByfFJGrbZqfV9UmYDrwfEyYausGsD/B/XRgItCsqkGP8NMjcVQ1KCItNnx9YkFE5FrgWoBZs2ZlVHjf6k+hz96NNhxE5p6InP7GjOI5HA6HY2hJOVITkaki8rfYtSyPMBfbMIOZl/olMB9YgdGu/OEg0ho0qnqzqq5U1ZWVlZUZxZHiCfgu/iD+d/83vjPfjPj8w1xKh8PhcHiRbvrxP4F5wENpwjwEzAU+f6QFUNXDqhpS1TDwG8z0IsABzL64CDOsWyr3BqBMRLIS3OPSsv6lNrxjjNMTamP94Vt5dN+NvNrwD8Ia6jeOw+E4dkk3/Xg58CNV1VQBVFVF5NfAf3CEav0iMlVVI/vf3gJENCPvBf4oIj8CpgELgRcxm78XishcjLC6Eni3LcvjwNsx62zXAPfEpHUN8Jz1fyzdfQ0E7e4k/Nw/YOcGUIW5J+I7+wokv3gokj/mefrAD6nt2gJAbdcWAuFOlle+e5RL5XA4xirphNps4NUM0tiKsS7SLyLyJ2AVUCEi1cANwCoRWYExubUH+CiAqm4RkbtsGYLAdaqmmy4inwTWAH7gVlXdYrO4HrhTRL4FvAzcYt1vAX4nIlUYRZUrMylvJoQfvBl2b4o6bHyCcOMh/O/8r6HK4pilO9jSJ9Ai7Gt7zgk1h8ORknRCrQsoySCNIhu2X1T1Kg/nWzzcIuG/DXzbw/0B4AEP911Epy9j3buBd2RSxoGggR7YvTnZo3ob2tWO5Bcl+zkyJttXQI6vkN5wR59bYXZm65wOh+PYJN2a2kvAmzNIY7UNe+yRlQ2FpcnueUWQk3SQgWOA+H3ZnDzpg/glG4A8fxkrKt83yqVyOBxjmXQjtV8AfxaRZ1X1dq8AVg3/A8C7hqNwYx0RH74L3kP4gZshaA/69Gcj578b8R/RbglHAnNLX8f0opNp6z1EWd6cPgHXR9OfoO1+8FdAxScg19nWdjiOZSSdvoSI/BCjBLIe+BewD7P2NQu4GFiJseDxn8Nf1JFn5cqVum5d/9vxtLcLrTsIosjEaUhuwQiUzkHLPXD4hui1fwLMfQB8Y+8YH4fjWEJE1qvqytHIO+1wQlU/LyJPAJ/FqPhHzLb3AM8Aq1X1vuEs4NGA5OQj0+ePdjFGjfC2F9GXHgafH9+plyLzlo9Mxu2Px1+HmqB7IxScPjL5OxyOMUe/c2Sq+k/gn3Z/10Tr3BBjueOYR2v2oK89D4WlyLLXI3nHzkhND1ah999M5LzY8KFd+K7+OlI+NX3EoSBnDnTEOvgge2aKwA6H41gg44UfK8TGj6XeIUKrtxP+6/9C2GwK1tdewPferyFybJwMrbs3EncAejiE7tk8MkKt/P3Q9ZIZnUkOTPwEZE8b/nwdDseYxWkzDBLd+GSfQAOgbj8c2AEzjhu9Qg0xD1dv5ZmanRRk5/Lm2ctYXBZzVly5hxCZMEJnyfnLYNpNsHs1aDvU/xg618L0/wfiqrZjeNGWOsJ3fh86moxDbgFc+0P87hDdUeXYGE4MJzkep0Nnjx91/rW1e/jr7pc51NXKztY6frblSVp7u/v81eMwVK16eeQKuOedRqBF6HwGGm4dufwdxyzhv/4wKtAAejrhtq8MaR7tvYd5ZN/XuHPblTyy72u097rJsv5wQm2QyMkXQV5h1GH+Scjk2akjHGVsaToUdx0Ih9jceDAmwLPJkV57PtltuAh7mPBsu3/k8ncck2hHC7TUJXu0NSW7DYLna35OXddWlBB1XVt5vubnQ5r+eMQJtUES9mfHLSkxztT5pxeWJbm93BBz8s80D63P0EjqEHmMlBP3sjkcQ01+kfeMTLZHfRwE9V3b0147knFCbRBoOAy3XA89MSp4rz5D6O8/HrUyDTVnTJqT5LazNdpD9S8+HbIS1hCmzBvmUsVQ+R8ejq5aO4YX8fnxvenjkHjW8Fs/N6T5VOQvSnvtSMatpg8CffVZ4odplj2b0I4WxMuE1lFGQ3dnkps/QbNTzrgcffYfEA5DfjG+i64ZodIBpauNgohG1/nIO2Hk8u+H3QdaeHp9NZ3dQZYuqOCsk6YxRIeuZ0zH4Vdpr14PCkXTV1A4ddmI5j9ekTlL8X3m14QP7YLuLvzzTxzyPM6Ych3P1/yc+q7tVOQv4owp1w15HuMNJ9QGgfYkN/h9fqEAmTZdwYZOWh/eSaC2ndw5Eyh5w3x8+WNjCu3hA68luU0tiAprbW1An73HCDSArjb08N6RUekH6KmKF2gAha8fmbz7obM7wL2PVxEKmY7PC5sOUVyUw4mLRs4oc2/bYVqqopvUW3b9m6yCieSWuq0PQ4H4/PinLxy29ItyJnPhrG8MW/rjETdPMwjSWs4IZ36YZfO9r9G7vwXtCdG9rZ7Wx3YNQemGhgMdyQvfsVOSemBH8r3uTxaEw0aXhxmz3hHMPw2Hajv6BFqE/YdaR7QMPS0HPNyqR7QMDsdI4kZqg0BKK7wmH41f8cQUPvGEuwIE6+NHfL37WwZZsqGjI9iT5NYQs4Yok+egCHHTsJPnDH/BIuR4aJrmLhm5/NMwaWIBIubs2KhbYeoIw0BO0aR+3To6OqipqSE/P58pU6bg87m+ruPoZURrr4jcKiK1IrI5xq1cRB4WkR32e4J1FxH5iYhUichGETk5Js41NvwOEbkmxv0UEdlk4/xE7OJFqjwGfT8+f7w6f4ScvIyt9EteFv6yeC2q7Clj49Ts/e1NtAaShdoLtXuiFyKQFXOv+UVwwlnDXziAYCPUfj/GQaDsvVD0upHJvx+KC3N4w5lzyMvNQgQWzyvnpOOThcxwkls2g6KZpyK+LMSXRdH0k8krn9Pn39TUxJNPPsnWrVt56aWXyMSAt8MxlhnpLtltwCUJbl8EHlXVhcCj9hrgUmCh/VwL/BKMgMKcmH065kDQG2KE1C+Bj8TEu6SfPAaPlwr/ALT/RITSNx6Hv9xYls+eUULJBSOoPZgGTTEOlZjVQt34RPTYHYCudmT/tmEumaXtfgjGbkZVyB0bzy7C0oUVfOydy/nUe07msnPnkeUf+VFQyazTmHL6h5ly+ocpmXNmnN+uXbsIR9ZDgdraWlpbR3aK1OEYSkZ0+lFV/y0icxKcVwOr7O/bgSeA6637HWrOxnleRMpEZKoN+7CqNgKIyMPAJfY0gRJVfd663wFcATyYJo/B47UBc+L0ASWRM62Yyg+dQjgQwpftH5JiDQWzisrxIYQThNtb5sRoeXkdXaThZLfhYDTzHgA+n+DLWG1oeBDf2KlXDsdwMhYmzyerasRsRQ0w2f6eDsTs8qXauqVzr/ZwT5dHEiJyrYisE5F1dXUeAisG7eny9ti7KW28RILN3TT8cSO1P36Oht9tINiQWqtyJAlpmJyExtAvwrySqPaenPj6+FO+K2bA7BFSqS+5zBwOGiFrKhRfNDJ5jxOmTYvXgqyoqKCkpGSUSuNwDJ6xINT6sKOy1KeWjkAeqnqzqq5U1ZWVlf2oXqdaN2usGVCZWu7fRuCAmfIJ1LTTfP/YsBrQ2N1BdzjeOkhIlT/tjK67SPlU5N3/DQtOhrnLkAuvHrlTv7MqYPafofLzUPlfMPtP4HcNcqZ0d3ezcePGOLfi4rGxnutwHCljQagdttOK2O9a634AiD0ca4Z1S+c+w8M9XR6jTuBwO4GDbXFuwcPtaHhYZXtGpNokvKu1Pu5aH/sDVL0Euzehd30f3bd1JIpnyJoIE94HZVeBjM6J1xoOoSNqGmxoqK6uJhAIxLnt3r2brq4UMxAOx1HAWBBq9wIRDcZrgHti3K+2WpBnAC12CnENcJGITLAKIhcBa6xfq4icYbUer05IyyuP4cGX+Uilc8OhJDdfeR7iG901GIB1dXs93acUREdDWlcNsUIsHCL8/D+Hu2jxdL4Au98IVadD9ceMVuQIEV77IOFffJrwz64j/Pif0DG4pgcQDgXQhDXIVJ2Ww4edJXjH0ctIq/T/CXgOOE5EqkXkQ8D3gDeIyA7gQnsN8ACwC6gCfgN8AsAqiHwTWGs/34gojdgwv7VxdmKUREiTx6DQVBa5KwdgpT+UPCLLrhjZvUyp6Ekx+siKMZMVVo9N5jUjuHlcA3DoixC0Jwd0Pg/1/29ksj60C33qr9DbDaEg+vIj6La1I5J3poQCXdRvvoea52/m8Lo76G7c0+c3Y8YMzz1pvb29I1hCh2NoGWntx6tSeF3gEVYBT0NnqnorkHRolqquA5Z6uDd45TFYdOd6b4/6fRmnkTWlCLYkzIZ6CLrRoDKvyNP9UGeMyndP8j42QplbUxk0gUMQSuhcdL86Ilnr4T3Jjof3wOLT45xC4TDBYJjcnJG3ddC293l6rQWRcG87jdsfZuqpH0D8WeTm5nLKKaewdm28IK6oqPBKyuE4KnAWRQZDZwotxQGsr4TakoVC1hRvYTLSHD9hqqdK/3FlMRuIH/9DckSP42qGjezp4CuEcMxJCQUrRyRrmXFckjUVmbk4LsyWqnqeXLef7p4Q82aUctnr5pEzgts2eloOxjuEegl0NZFTZJSgJk+ezNKlS9m5cycA8+fPp7y8fMTK53AMNU6oDYaU6yeZj7RCjR6L8lljYakTSnLyPHdXvXWOMe6iddVQ72FH0Os08OGia228QAPwl41I1lIxHbn0w+jz/4RgL3LShXH2QDu6Ajz83F7CVulnV3ULL246xDknz0iV5JAT6mn3cGuDoqhm77Rp05g1a5Yzj3UEqIYhGECG+Bw1x5HjhFqGtAe6yffn4I958X1zlxJe/y/P8OH2ZnxFZf2m6ytOfhm6Xj5E0anTR/yIkkTaAz2EPAT0vvYGluVOB3+KEYeMYOPYcl+yW8fTMPGjI5K97/gz4PgzPP0amrv6BFqEOq9OzDDRvPsZ0ORZA5/fnH/X3d3NSy+9RGNjI7m5uZx44olMnpxyC6cjAd31CuFHfgftTTD7BHyXXYvkj41ZlmMZ1zXrh9bebv7nlYf5/PN/5/oX72Z9XXS9LJymd6ZeChSJYcJKl4f2Y7i1B+0dwXWpFHQEvBUGDnYag8spj5dJPDR0OMk9LtktZ8HI5Z+GKRWF5ObEC/7Z00ZuH11Xjcfaogg5pcYmwbZt22hsNDpWPT09bNiwgdBIrocexWigh/CDvzECDWDvFvTZu0e3UA7ACbV+uWfPK1TZk57bAj3csu1ZuoK2sd+zOWU8X1b/0xGdrxzynKn0l+fjyx39QfRTNVWe7ttbjMp3yr1ZHc19P8PhMPc8toOf/uElbvnbRuqbhthaSt4yiJskLUpxGvbIk5Pt54oLFjBtUhElhTmctmwKKxaPnEFjr03weRWL+mYAEm08BgKBUdmj1rWllpYHt9Ox/iAaGptbIpJoroMEi0J62HsLjGNkcUKtHzY0xK8ZhTTMrdueMxdlKaZqisoymoYIHu7wdBf/6O9RA5iY6721IDJS0y3PeEfsiB6d8/v7trJzfwuBYJiW9l7uuPdVGpqjjUHvgVZa1uyg9YndhFo9NCn749D1xPUMfIwpqyLTJxVz5aWL+fDbT+Sck2fgG679h4EDEIrfxJ9ovFiy8iibFz3BIFHLMT8/n8LCkd1O0v7cfloe2E7X5lraHttFy792jGj+R0z5lCSFKJl1/OiUxRHH6A8HxjI+H+0e54ltajSGSmTBcm+VkAyt9Octm0zXpuSNrmPF9uOCEm/V7jy/OZVba/Z4R4xRoKlvSu75P/biPt5x0XH0Hmyj8c5NYNedul+tpfIjK5GBaAeGErZDhNsh1DqmBNuwEmqFg5+BrpdBcmHidVB+NQAFkxaTXTSZzprN+HNLKJy6NM6w8aJFiwiFQhw+fJjCwkKWLFky4uu4nRvjTcp1b61DL1owsDowCog/C98VnyL8xJ3QXIssOBk5482jXSwHTqilJ4V2Y58gq07Rq0w1gksgd3qKhndsbFNja7O3ZYkKe4aczFmCbv73gNPt6DSmmbo2H+4TaADhjgDN/9rBhDdF1eJVlX1tz1LftY3KguOZVRw/+sBXDOHYaTQfyAiu6Y02jbcZgQagPVD/Yyi+ELKNoWIN9RAO9UJPC6GeNrLyy/qi+v1+li5dytKlSVs7RwxfXhbhmBG65PhhDFjTyQSZPAf/u4buFCvH0OCmH9OQVZDalmBnsBc6Wz39ZPKsjNIPd6dYkxojL3VtV5une9Cue8jCFPvBlr6+72dJYbKAWTLfnAoe7khWROl5rR4NRjsTr9T/gWcP/ZjtzQ/yzMEfsan+z/ERJn+NuGpc/iHwxR+6Oq4J7ElwCEOvWdsJtNdRv+luumpfo+PQJuo3/o1woHvEi5iO4nNnQ8x0e9E5s5FROHPOMX5wI7U05JSnnsJq6ulk2vwVqM8H4fgRna5bA8ed1m/6wWbvBiZn3pAczD1oSnK8hUNTr50eTaUoctKqvp/TJhXSujsqvHw+WLbITGv6Sr2VacLdAfxFxm9H05o4v+1N/2JZxbuiDsUXQt6D0LUOchdB7sI0dzQOKXwdtD8WvfaVQf4KADrrtsfNNoSD3bQf3EDJbO8tCKNB7rxyKq9dSW91K9mTisgqHx2j1I7xg+sSpaHzQB0lHmr75bkFTC0oRfIK4fTVyRFr96Ld/a+L+cu8hUawfmysqZ2Y4rDTyflG2EtWtnfEvcbAcXNbD6/tjjdhFQ7Dus1mWjNvwUTP6L78aLp+X/xIL8vnIQizJ0PJG0dFoGnDQcJr/o/w/b86otMJNKwEDrcT7gr0H9iL0iug8guQu8QIuBm/Ap8RDL7sZAHRXr2eQHv6cwJHGn9RLvmLK51AcwwJTqilIxzm8ydeyNzCcvziI0t8LCip5D+WXYDPLqhLcVlyPFVo799SfPd278Yl3JQ8gusKNtHYvWtErcB3Br33qe2ze3NCgRTailb7scXDBBjAwTpj5SJnegmJJkskxx83/RQ3KkNYWvHODEo+MmhXO+E7v4tueRrdtpbw336EHsrcmHOwsZP6366j4Y4N1P7yRTpfGdg5fH1MeA/M/iNM/wnkRdcjC6csQbwE2+GRsY3pcIwGbvqxH6YUlPLFky9J6a+9XmaucmDitGT3BIK13ir9iWxu+Bub6+9CCVOSM53zZnyVgmzvUc5QUuLRIAI09tpy//su74jWVNTUFKcNHD/P2BYUv4/i8+bR9pgVBD6h9I2L4sIuLLuIirzjaOjeTmX+YkpzZyYmN2ro7k3QEzOq1jD62gvI1My0X9ue2kuoxQr+kNL2+C7yjq/AN0SGj31ZeeSWTqe7Pn6/ofaOjZkAh2M4cEJtsNQnWwSheAKSgako7enfekNHoJ7N9X9GrUpka+8BXm28m5WTPzzgog6UrP7uoTHFuVsv3gczj6O+2Xsj78wp0bXKwlOmkbewnGB9J9nTSvDlJVfJCXmzmZA3gON8RggpKk1WVC3KfD20T6BZNBAm3BEYMqEGkFM8NUmo5ZXPHbL0HY6xhhNqg6XxYLKbZqa9mM4UVjgUxuf30Rls6BNoEToCmR/cHexsonX/WgJtNeDLoqByEUXTT4rbr5SK/R3eU6h+O2cop16M7vdYR9prprcK873X3Frbe5lQEl1P9Jfk4S9JrbEY0gAdgVqKsifjk5Gtsi/V7+e5w7sozs7l4plL+tYTAZh5PLJoJbp9nbmeNAtZ9jrvhDzIO24i7YejBoezJhWSNWHo1pXCoQBt++OPlfHllpBfGb/2WFNTw759+8jKymLBggWUlBwje/wc4xIn1AZLZ0uyW4Ya+Tnzyump8hAcPvDZdaWJefMpzK6kIxBdf5tZfFZG6WsoSN2mv6PB6Bpd274XCAe6KJ13br/xD7Z73BuQZZdiZer8tFvqSotzmT2thL0Ho1sffD5h2qT4aclQew/Bhi6ypxbjS7CVWNu5hacP3kRPqIX8rAmcM+0LVOSPjELIxoYD/HrrU33XGxqq+fbKN5OfbZRXRAS5/ONo3X4I9MDUeRmN0CMUnjYD8fno3tGA5PopOnNop1Zb9jwX998DhHta4zo0u3fvZsuWLX3XdXV1nHfeeeTkHEN7/RzjijGjKCIie0Rkk4hsEJF11q1cRB4WkR32e4J1FxH5iYhUichGETk5Jp1rbPgdInJNjPspNv0qG3doNoMFPbTWmuJHUh1dAfbXtBEIxit5ZE1IMTqJ2bfjkyzOn3EDc0tWMblgGadN/hjzSldlVLTuluqkRg2g4/BrGcXf3d7g6d6LGWHqxlQbr035VZXGlvj1m3BYeeiZPX3XnRsOUffrdTTdtZm6X68lcCh+b9yLh2+mJ2SEa1ewiXWHf5NR2YeCtXXxtvw6gr18dd0/qe+OP85FKmci0xYMSKCBEYoaVgI1bfTuaqLxjxtpf27/wAsarIeO55LMZHXVbvMM3ttmpo27urp49dV4pZFAIEBd3djSjnQ4BsKYEWqW81R1hapGdvV+EXhUVRcCj9prgEuBhfZzLfBLMEIQuAE4HTgNuCEiCG2Yj8TES639MRCmeVmED6NdpuHbuL2O3/xlI39Zs43f/HUjNfVR5ZCc6SUpzk6Ll7dFOZM5Y+p1nD/za8wvy/wA73AK7UXIzBL7hFzvqbDI6EzbUmh4nvIGAA7UttHWkbyXbce+ZhM/EKLtyT19VkW0O0jbv/dE81GlrTd+ere5Zy/hxONUul6GA5+G6o9B+xPpbmlATMgtSHJrC/bw4P4tHqEHTvf2etr/vSd60rlC+7P7CHlsSk9Jyz2w6yI48HHYeR60xexZ8zh2Bsx+NYDa2lrMAfPxFBQk37fDcbQw1oRaIquB2+3v24ErYtzvUMPzQJmITAUuBh5W1UZVbQIeBi6xfiWq+ryat/iOmLT6pb67nU2NB9jZWktPKH5kJquu9I7U3U4gGObf6/YTtg1Hd0+Qx1+IHl0jfh++Yo91p0A4zqrGkTLYQx/nFHvbfuxLf7H3BnPfMmNR5EBN8gGVQN8ZY+HeUNK6YmyDLiKU58VPNSph9rbGGFLu2Qn7PwQd/4bO5+Hgf0DXprTlzpQLpy/uMwkWS3PP0Fiy797uMRIOK+HODPesaRBqvwNE6koQav476i/e66a59ugZL+FVVlbGhAljY/O/w3EkjCWhpsBDIrJeRK61bpNVNaJeWANEjCpOB2LnaaqtWzr3ag/3JETkWhFZJyLr6urqeOTAa3xl7b38bMuT/OCVR/j0s3/h77tfjkZob/JIxIdMmEJvIERvIF441TR00GMb8nBXgHCT916ucPcRbsaNIdWetqyC8rjrxsZG9uzZQ11dXVzP/ewp89OmH670XgMKP/9PwGy+Toe/MIec2WVxbvnHxx/NMjEvWT2+LRCjcXrgE0QbdQCFtgf7rkK9nTTveIy6DXfRuuc5NJzCCooHJTl5fP2UyylPGLGdPmlONLfOVsIP3Ubo998g/NRfUa/p6BR4bb73l+eTXZmhpXwNGHuPcW6dEGo2v72Emj8X8ZmldK9OTygUcmeqOY5qxpKiyDmqekBEJgEPi0jcwo+qqogMu6lfVb0ZuBngtLPO1H/seSUpzJrqrVw043iKsvPQ5z1OXrbTh4X52RTkZdEZY+NRFapr2pg/q4yeXR4C0eLzsJk4UPImzMaXldc33QQg/ty+40fC4TAvvPACDQ3REUNJSQlnnXUWWVlZtHjtwYvl6RSHIu7ZCEBbZ//TaFkTC+jd29x3HYw9libUwe6WJ5PizCg61QZuhKDHtoKYhr5p2xp6W80UZqCjDg0HKJ2XuYZils/Pfy2/iDXVr9LY08mplbM5tTK6vSB836+g2qxdae1eCPQg578no7QLT5lGz+4mgnZEmzW5iAlvW5Jx2QhUY+pazGshueAzQjF/8gl0HVwfF0ViOjpVVcnn5bW1tXHo0CFmzJiReTlGm9790LPDmAfLKu83uGN8M2aEmqoesN+1InI3Zk3ssIhMVdVDdgoxooFxAIgdJsywbgeAVQnuT1j3GR7h0+LLzSYQ9u617m1vZEnpFNi1weNmonFOWFDB2s3xliLKSqypp/wUj9/HkBwB4svKZeKyt9K2/0VCPe3kT1wQd/zIoUOH4gQamIMjq6urmTNnDm2BfoTavhSWKfoxEZZrjxXRUJjOl+PXzLo31xI+bx6+vCwOd24mqPGKLj7JoizXCpXOF70zKDCaneFgT59A60u/YfeAhBqYtbUr5ycbb9buzj6B1udW9TJkKNR8+dlUvG8FgboOfPlZffYuM6bhZpKOdKj4PIiZ0vZJ8khdwwG6G/eSVz6bjg7vzf89PUdwrt1o0fQnqPsBoCB5MP3/QcHpo10qxygyJqYfRaRQRIojv4GLgM3AvUBEg/Ea4B77+17gaqsFeQbQYqcp1wAXicgEqyByEbDG+rWKyBlW6/HqmLRSEmzrZEnZlCR3H8Li0ilwIPWBhhowo5QViyuTDoaMnDEW2O9t5T/TLQGZ0NO0h+76nQTaamirXkugMyrEUjVevb2m7E8c7OfARq+p1xgmpbDlF7JTnOGeoOcxO6F2U66i7ORnH9Ygjd3WAklOitMQul8CQPzZ+LLjpw79MUevDJqcPMgvjncLDXzaOLuycOACDSDk8fwL+hSB6a7f6Rmt/YCZPg8EkssqIkydOnXgZRkNwr3Q8DP6KpF2Q/3PR7VIjtFnTAg1zFrZ0yLyCvAicL+q/gv4HvAGEdkBXGivAR4AdgFVwG+ATwCoaiPwTWCt/XzDumHD/NbG2QlEF17S8NHjzyVxZaIsOx+/z4dmp5kitOsVVfua+xQjIjyx1iz7BZtSjGg8BoetvQfZ1/Yc3UHvvWNehIO9tO57gchLr8Eemque6POfMmUKfn/83YkI06YZE1/5vhQGiyOk0I6MsGCWt8KB3wp5zz160HcC9oS82eT5ktM43Gk3fKcyYGwbexEfZQtWIX4jMPy5xZTOPTttmQeC+HwwI96sF13t6MHkab109B5qo/XRXXTvqPf0D2uIQx0bqG5bSygcI4hKPYxpB6PPNKvA+/lLlnke4XDySG7JkiVHj/aj9kA44R3yEvSOY4oxMf2oqruA5R7uDUCSDrvVYLwuRVq3Ard6uK8DBnwa4n+9cHeSjGkMdNLc00lpKJhy87H4zaPduitZw62rxzRM/uLMeuevNd7Py3W3A4pfsjl3+vVMLUx6XEmEQz2QMH0a7Kijo+ZVCqeYxmvOnDns3Bnt0S9evJiioiIAZqRoFPuYfQJsSndIqPeQ88SFRquya2uK/VDZpkMQCvfSHU5upLY23M0JE1dD3U3e8Ysv7fvpyykku6iSUE8bBZOOJ6tgiG1m+j1eoS5vrU8vGv+ymd49zQB0vnSQ7BklTLzqxD7/sAZ5dP+N1HeZac7i7Km8Yfa3yfUXQ/6pyQnWfBHmG7X+svmrOLz+93HT4QClc8zm/alTp1JdXR3nV1VVxdy5R4kZLX8xFF0A7Y9E3UqvGLXiOMYGY2WkNiYpnDOVnhTacvn+7D5VfS/CTbUEQ2FqPI6RCYfNHqysmanNEakd3YXCvWyqv5PIaCukgeSDMlOQlVsMvuRGt6Nms8lDlV274q3K79mzp+/3EzXb02fQld4gc9/aYQKTK8xIQLK9q1/HWrPcua/teU//gNpn2p5CoAbMOlo4FKB+8z/obakm1N1C277nadu/Lm2ZB4xHFVCP44q86K5q7BNoEQLVrQRitkIcaF/fJ9DAaH7uannCXIQ9hGeo0WhFAv7cIqad9TGK574OX145OeULqDzpKrLyS01abcmHwB5V62kAU74FFZ+F4stg8jfMIbFHEb3VLTT8aSN1t6yn/fn9nvsGHQNjTIzUxip5U1L36kUEulKsiQHa0UJtIPU0TlNrN+HNKQwCA+FACH9uFiENJClL9IYyGwmEA93gIZQ1aBqu+vr6pJeoqyuqHFKT5v5MAumtX+yu9p4q3XeojUVzJqbU8AxaqyKHOjakSNmWOWcOBKuTvdsfg7K309t6KGmNq716HSWz4kc4qkptbS2BQIDJkyeTnd3PtGssXnsB+9MatfTs9p5+DfdE/7PecHLHIRBxy10AUmDU+CP4K/oURSIUT1tG8bRlSem0tPQ/ld0bCrKp8SAiwrLyaWRnYDN0ZAlB1iTImQuFQze1PBKEu4M0/e3Vvr2a7U/txVeQTcGJyWvJjsxxI7U0NKxLfehjjj8LXkuhfQdQWklJGrX8YDCc8uRrMNY1ADbV/yXJb17p+anzjSVVA2Q1KxM1HyMcPGhGOv7+zD51pxeuG1OcF3fIWlVJNf3qL7WHXJJKuNj7mvJNEI91Pdvoi8coFQ0Tijl6RVV58cUXWbt2LRs2bODxxx9PqRWYlFQoCNUeo9lUJ4InkD2lKMlNcv3kzCztu55RdCq5/ui1X3KZU2LtdmoQ8k+MiZ0DU76fUd5ASuEduf+uYC/fevlf3Pza0/x661N8b8MaejO8txEhWAt73go1X4aDn4HqjyRNtY5leg+0JhkfSBy5OwaOE2ppyK/sZ00pnWWJUC8daTZQTyjNR3JT93p9+dk0dO9ie/P9Xr7xl+3/hj3vgr3vg84Nfc5hD7uPxt2M1EpLSz39t20z011FWf3slevvdO8UUykR6/2+whRCy05LLp54uae3L7JWlzUBSt+eHKDkjSaZwkqP2IIvO7rpubGxMc7WYW9vb9wUbDrCD/4W2pI7BhHN1/7IP2EyvgQN0dy5E5AYbdlcfzEXz/4ux5evZtGEy7ho9ncoybF2A9rWGCsq0dKDZiaQ0/Haa2aL6PO1uzkcM1qv7mhmff2+VNFGnua/xe9T7HoZOp4dvfIMkKyK5JkcLzfHwHDTj2nILitO6RcKh+GM1Sn3aklvD6WlqTeCdnUHyCrIJYB3IyTZPvbUJm88Bnil/g8smfhmc7HnKuiNGVFWvx8q/wsmvJvuBu9TmCNGjru7vYVexH2whrp6UhytU1xghGXPbm9NtaA9jqWuw3ukHMaOFrpegebfxXtmT4cyczp2oMPriB4lHOzBbw9A9RqVxVrUeO7wbv6xZwOBcIhzpyxg9Zzlfaeesyt5Yz4ANbth6TnefjGIT6A7fuTTvb0BDYXjTv8uzK5kReV7kxPo8dCybL4TiqL78MKBbpp3PUlvczXZRZWUznt935paKnJzzQi6oTv52exqrefMyZkdgjrsBD3+X691xlFEVdlzsJXW9h7mzSijOGb2RruDxni5tf3pK8ymYKWnoSPHAHAjtTTkTEz98h/sbEm7dqK9XRxuSN1rzsvNwl+e+gyxYEs3+f5UQjFsTGB1vBgv0CLYvTqhNCccq4b7RmSJRBr140smefpnSme391RVZPwWak6hlGAPT91Q//v0GTT/0cMxWqV7U5wy0LY3OrrxGpXNmmX2v73SUM1t25+jubeLjmAv/6p+ladrYgRJqpHs3hSb0r1KWxA/WvXlZYEvw42K4pF/b/yUb8uuf9NdX0U42E1P836atj/U5zdnzhzPZJcsMVZNirKSp4fDCabXtLOV0B03ELrpI4R+9R+E96Wesh96PDpNvngTY8HuFuo33c3BZ39J/aa7CXZnviWmP8KhEOHn7iX0z18S9pqGBu5+dAd3P7KDR5/fx//dvZmDdVGh27HuQNSYNRDuCBBuPcoUdcYgTqilI5xaE6k8rwDW/StN3DDBUOr4WX4f3VtSH/GhPSFqu1I3jiI+aPlrishm+ktTWEMxnkowmH59pKE/iyL9MLUyec0IYPZUo/UZ6ki9pghG0zMtXrYtiy6Mxu/xVnQJ2MNPA4EAra3xYUSEsrIyALxMpD0VK9Qmz/EuV3HmBoGLXjcnetSQmOuMrcl0eGiHBnfETfv2tMQr0gTaa/tObzjuuOM4+eSTKSoqQkQoKCjgvPPO67MJuXhCssLC/NJoR0dVCf/2v6C+2vwXna3o336IDpHB5wgdgTq2NT3A/rYXCMeumXmtpybQvOMxY1VGw/S2HqR5x2P9xskUve0r6HP3wI516F3fJ/zyI3H+/3p6F3sOROtXMBRmXYx1ocSTzwECdYOfPj7WcdOPaQh2pm5097Y2clxtmvWFA1XMOjX1YZYt7T0QSCN0soTCsNeaUAz5y6H9oWT3PGNVItDhvZkXQHx+srKy0gq2na2Zn7DtxfLjKtl9IL5nnOUXFs62jX5n+kX9bArpJblnLZFqW/p2aH843rN7o1EU8RUSSDCRFcFvT6/OysrC7/fHTTeqKm1tbRQXF9PlsTYWqy0qBUXe+xRnHp/2vmLJm19O5UdPJXCwjexJhfhLU4/ek+jZ7O1edxNM+hwA2UWV9DRF66k/rwzxR0eH06ZN69tsn8jsonIqcouo77G2KcXH3KKoRnD4mb8nnyeoiu7agBx/Zsa3EWrvoXtbA768LPKOq0BijmOq79zGo/tv7Jtynl50Kq+b/l/Gs+yt0PqPqK3P7DlQGJ9vb1tN2uv+2Nlax5OHdpDt83PBtMVMKzSzN+Hq7dAS3ynVF+6Hk0ynqjcQ4tWdydqtoZiOrr84h8RuW7hr8IbMh5LuUID1dfsIhsOcUjmTouwB1M9Rwgm1NOTPmpzSL6xhSDO9R1eb5x61CIX5WfQEUq9aSY6fttZDKf1D4QD+okug7n+SPXPNmkeon6mW/kZqg9Uja/U4Fyzb8/w4b8IpSqCRNbXA3mTPrvVQ/zOYdD2BTu81u+4WI+xEhNLSUhob4xufiODyUl8/2NFCbyhIjj8LOeFsdGvyaElKBnZ0i2T5CHcG6N7ZSP7iyrgpybrO13iu5md0BRqYXLCU06deR35WmfVN8Q91R6cAS+e9nqZtDxFoP0xW/gTKFl6Q8UiwqrWuT6ABBDXMkzU7onYwX04x6umJdgY37ajj+VcOEQyGOen4SZyxPF6ABhs7afj9K6idcu58+RDl7zkREaEjUMdj1d+MrqECB9rX0tKzn9LcmZB7HMz6E7TdD74Ss/E6YTtDTvGUOPufOcWZq8vvb2/ihxsfJWRnBNbX7eMbK99ESU6etzm0mJmRVEsPKxZHR7o5C8rpfi2+45k4HT1Ygt0tZrTaVkNO8RTKFp5PVl76NdUIvaEg33t5DYesstB9+zbx3ydfSmlO/yPk0cRNP6aheF7qRdtDnf3Mzbc1UduQetE6FFbPjbsRwoEwh3tTb36u7dhuzhDzouVPNo3003vDTY7H5uqunhAbXstsBJiXlXpzOtC3yTiJjqesfwqh3Rv9XxYtWhTXyE+cOJGSEpNvbIMeIYTyiy1GgSfs926ANJS5ik2wtZvaX7xA60NVtD26i7rfruuzfdkRqOPR/TfQEThMmCCHOjfwyN6vxsROoT2bG7WJmZVXQuXytzPljGuZdPK7ySlO3VFLKpuHGa04t6D3+o9UGMFV19jJw8/upa2jl66eIM9uOEjVvviORufLh/oEGkDgUBu9+8y7tbH+TkKJR+uQsHcvdx5UfArKrwF/cmNdtvB8ckqmgfjIKZlG2cIMt8MA6+r29gk0gK5QgI2NZjrXN/uEJLufsiKadkVZPol9h8ryfObOiJYx9nSKCIknvw+W/qZfOzo6ePbZZ7n//vt59tln4xSnNjRU9wk0gNZAN8/UeCufjSWcUEuDPz/1UPvZQ7vTR66uYuuuFCdDA+39HMsSaukC0gmlIHjYRewj3JW6UR8hUhlH2Heon03dlqKsfhRVvNT5gYFMQJSUlFBebhRy8vLyWLgwOmWcSjRtb7FC+Z6fegfYtjajvFWVhts3QDD6oLQnROcrZopsR/NDaEIp2oM17Gx+3FyI95oleSuSnHwpBHA6FpVNYkZhWd+1ICwoiZkS99oHWVgG080zPFib3CmorklotL3qiF3LbujytqGZ6++nsxNDVl4pFcvewrSzPk7FsrdkPEoBc55eklt2dJQiH/guLDkbps1HLv4gvrOu6PPLz8vm0nPn9XXsplYWcuUli/v8w50Bujd7dO6G2KBIf9Ovr7zyCo2NjagqjY2NvPJKdB3Zy2JSYn0cizihlobs4tTD7A6PXnwcPS10pBFcza3pR1HhdOttQJYUQMvfUwfwDX6KIN9r8/IAOFDr3euMHD3TH4e7U6wZRehJcYpA8WUZpQ+wdevWvk3o3d3dbNiwwdPQbyx5WVZApFKIyMnMTFbgQFvfJvtYQq1W0SfFIa+7Wmxv20N4AVCcZC71iPCLj7fPPanPgqei/LFqbfScvXIPa/4nrjJKTEDlhOQ62N0Tf7/5y6fEmUvLmlTYd3BsWcyIM0KOFFOcnZCvqrfS0CA5e/J8ZhZGO44LSypZGnPPvrx8/Jd8EP+VX8Z3QrI1k8Vzy7nuqpP41HtO5qrLjic7pt6H2ns8BZjkDK3FlsTp1sTrpqamlNcrKmZQkRftOBVm5Y6d7RxpcEItDb6c1L3brgxWnLp7UofJ8qevvMF+To0+1L4ZutIYE+5HczCUwQnNqc6Sy5TWFPfQ2o/WYwSln5Gmv8zbvfRNGaUPyS91d3d3yv17Ec6bssD8mJriZPBJKY7ESSAyzZiIv8wIxZ6Q94g2LzLNVvJW74Rl6BbztzTVxLW9PeEgGxvsUYRehptjzheMWI6JJdEgQXZlIRPffzJF586m5KIFlF91Yt/mc78vuXNw5tRPxa8JNv0Jdq6CqrOg7keppweOgLysbCrzo436zrZ6XmtObdrOCxHxXEfOqizEPzFZ6He9MjBFlv7ob/p1woQJKa/z/Nl8ecUlvGveKbx1zgq+evKllOdmeCr7KOKE2hGSic2INBr99HSnFxg5xekbppaOfl6uYHprH6msjcQy2OahLsXROgdq+7FEkik5M4yCQCwyAbIyVwaITD1GyMvLIz8//Si3MTJSmTzb018qZ3q6JxJMoUgUOVst1ZaOWcVWwy/XW2uRtqFTW5+Yl2zhYmKebdi8DDcf3tu3lWT9q8l1tKggeW9dVlkeRWfMpGD5FHwxI5XDnckj9UMdL0cvul+Duu9DuMWcpdZ0h7GyMkTUd7fzUox907Aqjxx4bUjSFhHK3740up3Dov3M0AyU/qZfly9fTnl5uSlPeTnLl8ef/lGYncP504/j4plLmJB7dFg7cUItDUNx+nTKtH39iAx/P3n3t14WTL2eBxDox5STqjKroCx9Hv3Q1TPM8+/hruS9hGVvJWmFPg2LFy9mypQp+Hw+SkpKOOWUU/r938ty7Mu9Z1Oyp8+PzE02HuyFpPiP8xYZtfmeoPdIrTDHrmsd+E/vhGu/m1H+mXD25PksitmbdvqkORwfOTh3wckeMZSwXW/u8FBPnz4peR0wWN9JyGNUH/Ywxr29ZQ3BsA3b7fH8vdzGKP6SXApPmxHnVnDSyB7QWlhYyFlnncUb3/hGzjrrLAoL40diNZ2t3PLaM/xo46M8U+N96OxYw6n0jxJlJXlpR0LhnkBK5TaA0oIZkG5ZL29O2vxD/ZzQLCLREckRkuWH4CA6nlnkEyS5DH2Gjrs3Awnrdr39nNadQE5ODitXrvT08+GtLLK1uYbVLIeK6dCUMBo5c3XfWXr9kb9sMu3PV0Mwmkve0kn4ck38fP8E2oLJ2zoKIpZmQh5bGsCMXIaIHH8Wnz/xQg50NJPt8zMpVuNvfQrjAzvWw/QFnkP9RXOiI+NwT5Cmv24hcLANBApOnkbJ+dE1m7Bnxy3MvrbnmFe6CvJPwpzZF5NR/ikDub20VOQVcUrFrD57lz4R3jA98z2ImVCwfArd2+oJNXYh+dnkzBvYdhB6q+HwDcZkXP5ymPx1M4MxBATCIX606dG+NdRtLYfJ8vk4fdLYPm/vmBqpicglIrJNRKpE5Iv9hR/c2UbZzJicerheVpIPaewF589Kf5jl1NIlpP37PFShY8nJS6E5F0NxfwoPBek1yWZ49MoHwtKKd3i6nzPNjlBy5pDUL8uJrnNJtvf8vy87s2mU3BQnf5darTjf+e+FwphnMPdE/Ke/MaO0wUwzVn5kJXmLK8ieVkzRqrmUXhzVvlw+6T2e8TpD6UfhabVij5DphWVxAk2DAXMwoBd2HWrWtPip4YmlueTEKEt0vnzICDQAhc71BwkcjvbUsv3eU/ASUV3JXQCTb4SsaeCfCBOvg+LMVfYz4UOLz+LaxefwljnL+epJl3G8h5WVwdD2+G5CjUZoaFeAlvu3952lmBGHbzB7Mwma78M3DFnZdrbWRZWCLC/1c9zUWOCYEWoi4gd+DlwKLAGuEpEl6eJoIPUU3wn97fd5/VtZOCu1QWO/Xyi65LiU/v1twpxYMB/yzkodwJd+TS4nJ58FCxakDfPWuSel9ZdUDfhFHwSgpMR7bSo3x1Q7n8dCeSyLyy8nO0FtvTh7GtOL7bRXViVMuj5q7y//NCj/QF/YySvf55luxYnewjKRt81b4en+Trv5WApL8V37Q3zv+zq+j/4I/1s+k1G6sfiLcih702Imvmc5RadOj7PQP7P4dGYXvy4ufLavgLLcyFpeik5HSeaC9YhJM0XrO9lY1bj47DnMm1FKTraf2VNLuOKCRXHhQh5HL8W6HV9+hWf6M4pOi16UroZ5D8D8R2HiRwZwA5nhFx+nVM7ikpkn9FkTGUpiD4QFCLf1Eu4cgFWRrlfSXw+CiblFSWfXV2TQGR5tjhmhBpwGVKnqLlXtBe4EVqeLsPtPD6b0+/AJ58AVn07pLyddyLJFqc1c+UQoOq4SvFR4fcaC+4LSi9PE98O0b3l7TvgEAPlTUwsl8flZvHix5/pRZLH4hPJpXD7zhCT/crum5DvpAuS0yyAr2zRyxROR992If6lRb16e4v7PONEoOBSfkUKhItdUSxHhojnfoixnDgAlOdM5e9pn48OWvQPmPWI+M2+GmD1MPp+faWdfx8QT30Fu+VwKJh3P5NM+SFaGL+a5Uxfy5tnL8NtXuyK3kO+dtjqqKGHLKJUzkGFo8ABOn/IxFpZdTK6/mPK8+bxu+hfJimgFTvAeyVF53bCUJRbxZ8HpHkcDnft2xCqQFBXkcMUFC/nku0/ibRctojTh/LzI2mFfmrn+PnV+gEUTLqE4O14ZpjJ/Mdn+sW3RYiDkzI6vN/6J+fiL+jnyKZb85emvB0FlfhGXz1rWd9TTjMIyLp4xtNOvw4EcK8eHi8jbgUtU9cP2+n3A6ar6yYRw1wLXAsyaNeuUS36fvOg+MbuA75xxBQChH32EpJWXibPwX2OmAf768Db2HYxf95k/s5TV50enmZru30bPq9aOXEEWkz54Cr78bILhHv6yI/nIkTfP+wWF2VZgtP0bDn0B6AHfRJh7P8RM2xx85udJ8bPLZlJ5gjm6RlXZsGEDNTU15OXlsWzZMioqKuLC3793M/fu29h3/ZHFZ7Oy0lvzL5EtVfU8tb6a3kCI0qJcLjhjNjOmRKex6m97iWBdvBZg4ZkzKT4nPv1AuIvsIdh7N67QMFSdD9ocdcs/E2b+cuSKcLCK8L6tSHYusmglUpx6dsKLrtfq6HqlBsnLouiMmWRPju9wdAWbWHf4Fuq6XmVi3iJWTv4whdkVKVI7+gj3BGl9dBe9u5vIqiig5ML5ZE0cgJbhMK6pRWjp7aIt0M30grKMledEZL2qei9WDzNOqCUItVhWrlypL65dy5MHt/PIgdcIa5h3zl/JSRXxI4zQ03+DDY+DZMHq6/DPiDdkvKu6mYef3UMorJyyZDKnn5hCFduDQLiLDbV/pLZrC/NLL2Bx+cCnllr2vUDH/pcAoWj2aZTM8NJaS8/u1nr2dTRxXOkkpvSzljYQNKzU/99LfesKvqIcJr5veZ9au6Mfwj1Q9z3o3g5F5xlzUTJw6yEOx1DihNoIICJnAjeq6sX2+ksAqppS/3nlypW6bt26ESrhsYuGwvTsakJ7Q+QuKO/T/nM4HEcnoynUjqXWYy2wUETmAgeAK4F3j26RHADi95G3ML22p8PhcGTCMSPUVDUoIp8E1mB2gN2qqltGuVgOh8PhGEKOGaEGoKoPAA+MdjkcDofDMTwcSyr9DofD4RjnOKHmcDgcjnGDE2oOh8PhGDc4oeZwOByOccMxs0/tSBCROiDWFHoFUJ8mivMfv/5juWzO3/mPNf/ZqpraTuBwoqruk+EHWOf8j03/sVw25+/8x7r/SH7c9KPD4XA4xg1OqDkcDodj3OCE2sC42fkfs/5juWzO3/mPdf8RwymKOBwOh2Pc4EZqDofD4Rg3OKHmcDgcjvHDaKtfjuQHUOCHMdf/iTljDeBj1r8R2ADUYI6oCQItQA7wZuCLNvwVwJJ+8gvZtF6xaZ1l81hjv7+FMSpdZ90CwGFgV0xZTgfOtNcN9vv11s8HvB/4GbASqAX2AxttvqcnlKc95veN9v6nAX+1brcBbwceBy4GVgH3Wb9bbN6LUzzXTcCrQBcwJ8ZvlS33NntftUB3iuc1B9jsVd4M/tffx1xHnmnQXr8f+Jn9/QTmqPJ3xuYDfBb4pXklktLfA1QkuF2R6nkk/P8H7fdfgII0Yds97mmbxz3dl/Afhu1/vTmSR+JzTPgvzgK+YsvUV08we4x+m6Z8LwAv2mfRiHkvdgOHbNzngMsS6v0WTN3/PDF1NSbNKcCdwE5blkNAr31mP+vnufbaOrfB3u9nE58v8GWb537gB8B/Wvev2LJttM95uy3nS8BZGdS39pjneR/Rd/D9wDTrt9Y+r5Up3r93Yd73A5j2JdLubMC0NYnPKhTjrzZeAHgI+Jt9husxBtsX2fCbbNgWzLv5T6AssQ2wvzcAX7e/m4H3par7adq5zbF5YNsT+zvp/xmuz7E2UusB3ioiSefBq+qvgA5gH0aI/ApTQTYDT6lqr6req6rfs1GuAJb0k1+Xqq5Q1eVAOfBdm8c8TIV8I/AGTAWdhmkkwsDx9rse05Cdham0bTbdZcCLqhqOySty3PEXVPVE4EJM45AWVT2oqm9PcP4T5ry5WFZjGoGrxBBbdxQ4D7gM2Kmqezyyeo+qzgMWA7kikiMiQ3VKRAewVETy7XXkmaaiC/jvBLcrMfedKVcBT9vvdPmU2O9eTMcpiRTPoQcoz+CeAraOLU2Xh2WVLe/lmLoZqSf7I0VJUT4BTgKqMYLny8ApwNXAJZijnIox/z9E6/0JttyXATd4pHk38ISqzrdluQRTl2ptGH+K++jC1O3zbD57sI1mQriIUOtLxx4WfDlwss2zA1hl39EvYd7Rvv9kgHX0/cA0+24cD3R6BRKRC4BvYzpE04GbgO/Ye1mhqr1e9xzxt2X+CvBrYAWQZZ/hKfYeJmOe0Zsw7cg/gD9gBOd1Kcq+ASMEj4SumDqYKo/Pkvz/DA8jITnHygfTI/8S8G17HTtSuxHTkHwHMyqpw1TKnZiezi4bvx7TCPRiejFt9rsLIxD3Aq1AN6ZC1WCEVch+gsAd9neP/a7GjGZ+b90iPcgezMsbtvm0W/ewLVPAxteYtBttvE4bPox5Cepj0vwApuGIpKHAVowQ/4ENr9avE7jLpt8Vk/8LNr8TrFubjbcd0zjtt3Ga7bPaap9PJL/9mEbtupj76LDlq8U04JFn1ovpCX/XPo9um4ZiRl6RfJ4gOpoN2u9izAsbtm5hTMMcItrj7sD830/aOF0xzzBsn+kPgMcwPdHt1j1SJ8T+h132c4DoqCPy/3TYsIdtOYI2jxft/UbybcT0vkM2j/swdShsn1MXpgd8ms0n8ux32HSaYtKL/Pf3YOplxC1s038x4Vk9BnwvJq/GmP8xUsdCNq32mDCRehH7qQWex/znkfS7bflaY57NTvvsu+3zjoQLJXwi4VtjytwO3G6faSTf2PcqtjztNo1a+7s9Jsw2ou9U2LrttvcVtM+/GjO62090FuWw/d4U838FiNbxEPBoTH6R/7jDPsM/E20vWjDvSLv177JlKAA+bdPeiBnZtmMEaMD+72ts/C6b534bvonoe/wypsPzmvXrtM+pB1N/ujAzFbfFxO2xcbfae2+38bqBn9t3Z44N/xJmtFsLrLV+92LelQM2zBbMiP6lmDZ5YeQaOBV41qbzIubd9QP/gxn5bgQ+6kZqyfwceI+IlKbwvxNzIvZvMBWlBPOHbcE0YlsxvbAuzPThdMwfEXkRBdMTzLK/s4GJmOmX3ZiK8qi9/gbmpSgD8oCnrHsZphL0EH1Rqm18bLqRhhJMTyyMqQCFNo07gEdsmcBU5i7MC/xLG78ZU3nq7H2cDXzQfj+Emc5QTAPaZcv4KqZ3v9yWI3FkMAX4P6AS+IhNR4CZ9jvLlvUdtpzft2Vrt884gOlUlGMalBPtM7oKMzo+3Za3FiPgptl8O2w592P+p0gv+STMi9OLeQGbgVJMRyTXhsnCNEyn2OsyTIP8I4zwLrbuhzAj0u9g6sYMzMv/n7a8t9k8Ig1aL9FG7BVMA18OfNQ+22xgNmY2AKIdobPtsxHM/1KCaXjut+m+zj6zRvu83oLpnRdhBHiuzTMP+BdmRmAjpn5FptN9mEYETN3CpnWBLccH7TM+jBH2PTbMn206uTH559l7/iOmzmHjrLDlWmrzbcYIiP+09/F3+6w32+cfGTFm2/t9xcZrsvcT6RxFhHMBZrp8LdFOS6TO99j7eMbewwv2PnNseRuJjkyn2d8lNnwN5r+9E5gKTLDP4pOYuo2NP8GWf6Z9np3A52weYXs/hzDvZOS/BSPIg/ZeCuy9/9vm+ZyqFtp8ioAPAV+MuecTbPkh2g7cYsuwA/iFjRfEdBrVlu0nwNuABZi2YS2mvXgJ8z77gHOIsh54jw1zLuZ9P1NVC2waHxaRiZj6LDbtk+1z2mbTOAezLDAdI7ifUtUzgRYRWWHDfAD4PxHJwdStz9hR84WYd+dDQIuqnoqprx8Rkbmk4ZgTaqraivlTP53CfyPm5bkG8/LXYF6oZzD2zY7HVOSdmD/7fzEvlh+YhWlo64mOzjZjphoCmEqLjQtGIJRhXoLIKKrBfnyYxnSq/b2G6EsYGSVEXpI3Ee3BbsE0HlcSFa5ZmBc6C5hv4xTb+5mDeZkPYxr6OuDDmAp6Kualm2XLEMYIhDttWQXT88I+p532PkuBRlW9lejoM9uGrbPx6oAzgPyYMqzANABXEJ0y+p0tdzmmV3465qWttM83MiUbsOHmYl7wyFTHpZieotj72o8RoH8FskSkmOh6VU3M883BNCaR3mKhzbcL87J3Yhq29Zj1kTr7vD+OEfzZ9t7ybdyTMGsdWZhR50k2n2KM4AbTEDVgOkmREUaxTetPGOGwF1MnSjF1MRvTcSi26f3B/u7BCEy15b8EIyz9REcptTbftTb8mURHCj/HTJNPxQiESOcoy5YjUhf9RBvci4h2DC61fj5MfYlM4xVhhHIuRhiXY9aIl2DqGZj/dKd9jnvsM8mz97osJu+ALe8pNh8/pi512vA59jmFMe9htk3Pb591JJ02e38BTP2ptdfnAQ/bdMownaPI+xMZee3B1Os2THtxHOZd9WE6ePNsXlnWTe29HrLpvIapC6vsszlLRNoxdXUCRohFZm7+F/MfCaZD7MfU852Y934ppiNZYu/1/TasD1PnFtjrmzDvkc9+P2HDF9kyKeY/f9SG/y5mjf0WEenCCM8cG/ckG+duzPtTAOy0g4YczH8YeV5n2t+/BT5gp5ffhekMHQccUtW1YNppVQ1i6tTVIrLBlmmi/R9ScswJNcuPMT2AwhT+P8I8vG7MAxS7lvYvzJ8c6c1dimn8f4KpgC023r1Ee7ZCfIMQWZ9QTAW4D/M/BGyYYkxvswMjqCLxFhKtdD02/kP2+1FMTwlMA3DY/o5MpdTaPCJCwI95ETbZZ1FFVDiVYHrXkZFgty1zLtGeYTdmtIeq/pFob3BRzH1MshWxnOhIs9deK6YhqsO88D/HCOJf2bBLbJmzMSOH9fZeKjCCJtv6fxrz4kTosuGujHl2kamnnRjhtsDeyxPW/9v2vmLXqz6IETR/t+sEkc6DYKZTXo9psMowjdEi+6w+atM5DdOoRqaDApge9N9tOiswL+suzJTvXbacf1HVSKMYYS3m/7/PhonwTVuuXpteSFWLVPW3RKe/HgAm2d+/woywHlDVmTZMRJgdb59jD0bw/QnTKYvUlUcwz1wxjU/kWUSIjP6exjRiihkxNtvn8lbr9j1MY15LdAYCTP3bRLSzF5lyjNxHpKcfmU4PYzqPOzBC6wUbrtM+v3NtebDlLSM6eo7U4dcRFcbrbVjFvGMR4fNdu4YVxjTIkfqLdW+yafpj7uVyjAAJYzogufb5PGjT/Jwtw+M2fGQGYx/RzuzZwLWYOpqHGWkHMG3NWky9+ppNtxjzP+/HvGOXEJ1efpVoHWkl2v5E/ts64KCq5mPq4t+I0qOqkenLZzCCcJktwzbr/labT+R5zLblP430/A3Tdl4OrFfVhjRhBfhUzHrjXFV9KF3ix6RQU9VGTEPyoUQ/u8i7BlPp/o55mYtFZD6m8dqCqViKqSyLgVsx02kTMD3zEqLTR5GXP9JjVowwFczI4xb7uwXTUPoxlQ1MpQXTEJxGdEqt3ZbxDHtdhanMYF7yCszLEBl5TLHffhtWMI37dMwoLwvTyM628SNrIJEpx9h1vpMwL8WbgbCIXBaTR+QlaLDP5h5MDy8PM533CEYg+jAN2AOYxjRgy/Vhop2BSCN6PqajEALea8soNvwnMI125BlvwjR0dxNt1Ooxve8QZsSAvd/ItODHMC/+cTHP6WO2/H8QkfPsczto72MupkHwYToP38I0KBXW7x+27JF3K9KxWINR+AH4D/vsp2I6E5da9xV2SmcW0bqz0T6fj2BGG7NtWUox9cKPaVzCIvJ+EZmHESZ59vkX2fwjShjTROQcm3aBLecTRDs8IYwCyL2Y+tyOWWuLdM5KMP+v2LwjU+xqy9Rl7yUfI0yKMFqZOzCj2BJ73+1ER+MRZZjIdHCEfZipwcjIKj/Gz2efdw2mcQxiRt4zgettfpHZhGzMKKYWU+/biXYKILou2mzTDdpn/kERKbLpvA/zn5UC2HoxOaG8vbaMkTWkidY9hBFUIcw0+mFMx+skW7YezIguMho9hHkvTrDxZ9q410fyt6h9RhWYOtiE6WBHhNdT9jvHlmWPvT4D8w5XAHVWaScSpo8Yhbr7MPXQp6pbMZ1+wbzPa2zYk4iui59JdLp4sU0jG1gHoKrdNt4viU7nbgOmisipNr1iq6SzBvi4iGRb90UikmowYp/KGFDgGElFkZjfkzFCIlFRJBvT4zyMqVz3YRrIevtHNWB6squIqmtvs+G7rVst0Tn0VqKLxwHMC3OejdeCaUiaMb3NG2yYXqILvIppwIMx7j3Ap4hfCI8oigSI9mYjygmxShKbrdsB+x0RYPswQuaHmIYpdsF7LfBTG77burdgGvDIYnsP0cXuSzC91MjCfkQpopdoI1+NEd6RzkHkeQUxPcyIgIz4hzAvReS5RNKJ3NcezChuc0xaimkoI1O1kam1Ovv/fT8mj58RVRSJ5NmF+b8DmIbgoZiyhDC9749gRoxtMc+93t5fO2ZtVu39PJLwX/Rg6tcnY8J0YwR+5N5+hGm4Q9av2/4XZ2KmPxVT9wI234Mx7iFbhr8RVdiI9OIjI24lqujwDEZIR7Q11f6nG+3/2JMQP/a/68W8T5H6FKmvjTHPMjJd2IZphCMKJoGEdCPrkc2Y6bmeGPfImloXZtT8MNHZiUiYDqLKR5G02+3/HrmHSP2Ifc9aMeulL9s0P2OfTaTc2+1/oZjGeC9GeD5kwz9IVFGr18ZrsHHPj7m/dsxsQKRO9NrndRtRBY5eTB26HdMeRZYyvkhUUaQXI/Qj70rkGUbubYv93R0TZl/MfxVRFOm2+a+xZQhhRoLLidaPzfZ/67b33QFcadvOgE2nHVP/tmE6ARFFkY32swN43MY5w96fP6ZNPhXT+X3FfkdGzd+JKcPjQGm6dt6ZyRoEIvIz4GVVvWW0y3IsISJFqtpue5g/B3ao6k1DkO5y4DeqmnL6JCbvAszi/rWq+tJg0kwTt11Vi2LyzMKMQm9V1bsHmt4A8347sFpV3zec+QwWEflPTCP31WHMowCjtq4iciVwlaquHsL0j7l2ZDj/t6z+gzi8EJH1mN7K50e7LMcgHxGRazBTJi9j9usMChH5GGbE9dl+gt4sIkswU0W39yPQMk2zP24UkQttng9hRsjDhoj8FDMlell/YUcTEbkbo7hx/jBndQrwM9uJasaMsoaEY7EdGe7/zY3UHA6HwzFuOCYVRRwOh8MxPnFCzeFwOBzjBifUHA6HwzFucELN4cgQEXmbiDwmIs0i0iMi20XkRyIyrf/YR5znEyKiMZ8mEXlSRFYNV54DRUTqReTG0S6HwwFOqDkcGSEiP8Rs2N+F2YNzEcbKwgWYbQXDyeOYfWlnYjagdwMPiMiCYc7X4TjqcCr9Dkc/iMibMOaNPmTtWUZ4UkRuxgi4waSfr6pdaYI0qurzMeH/jVEtvwizMd8rTQFyrfUGh+OYwY3UHI7++Q/M8Ri3JnqoakhVH4xci8j3RGSTiLSLSLWI/EFEpsTGEZE9IvJDEfmqiFQTtVOZKZEjUSJn6CEiN9ppwHNEZC1mNPcOESkUkZ+JyDYR6RSR3SLycxEpSSiTishnROQ7IlInIrU2XG5CuNeJyCsi0i0i60XkLByOMYQbqTkcabA2587CmA/LhEkYsz4HMScJfB54TESWavyhru/GmDH6BP2/hyLRwyonEj2K5MGEcAUYs0o/wJgnOmjd/JhDJeswdgS/grHHeHFC/M9j7Dy+F2PQ+bsYk0g/sIWYZvN8EXMyxTTMqQAjc/ijw5EBTqg5HOmZiLEfuS+TwKraZ23CHq3xHMbG3TkYs1qxXJ7h9OBbiVqBB3vQq6puTwiXD3xOVe9JcP94TJmyMMZsnxaRWaoae197VPX99vcaETnb5v0D6/ZZzAjwjaraadPrwBwv5HCMCdz0o8ORGRmZ3hGRS0XkWRFpwUwRRg7gXJQQ9NEBrHc9hjH2eirGtNBPMQcrvsGjjImjN0TkfSLysphzugIYA7leZUo80uNVomcAgjkp4uGIQLMMqw1Kh2OgOKHmcKSnATMymtVfQHtsxr0YQfY+jLZi5HigvITgh8mcJlVdZz+Pq+oXMALoux7hehPK9BbMobjPYY5HOgNzOKdXmZoTrnsTwkwherAoAFbAtQ/gXhyOYcVNPzocaVDVgIg8g1l/+u9+gr8Fs271LrVGVUVkdqqkB1m0rZjtBP2l+Q7gBVX9RMRBRF5/hHnWYNYM+7AW7Iu8gzscI48bqTkc/fNjYKU9GSAOEfGJSOSA1nwgoPFWwt8zTGVaijmTrD/yiR68GuFIy7QWeIMVZBHekiqwwzEauJGaw9EPqvpPEfkRcItVnrgHM+W2GHNK9h7gX5gDKz8rIj8G/onRmnzvEBShXEQi05jFmCNhLsNsNeiPh4Gfi8hXMAfRXkbyCC9TfgxcB9xnn8c04EtET7t2OEYdJ9QcjgxQ1c+LyLOYU6r/iBkB7cGsof2vDfOAiFyPOZX8I5h1rMsx6vWD4TybFpizt6qAj2JO1e6PXwPzMKc452GE3LsxJwsPCFU9ICKXAT/BnKa9FSO0E7UtHY5Rw52n5nA4HI5xg1tTczgcDse4wQk1h8PhcIwbnFBzOBwOx7jBCTWHw+FwjBucUHM4HA7HuMEJNYfD4XCMG5xQczgcDse4wQk1h8PhcIwb/j+NkZd+neuJLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x=\"Car_Brand\", y=\"Car_Price\", \n",
    "              data=Car_data_target_seat_door_fuel, jitter=True,\n",
    "              palette=\"Set2\",  # 设置调色盘\n",
    "              dodge=True,  # 是否拆分\n",
    "             )\n",
    "plt.title('Car Price for Different Car Brand', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Brand', fontsize = 15) \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10065 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           10065 non-null  object \n",
      " 1   Car_Price        10065 non-null  int32  \n",
      " 2   Car_ORC          10065 non-null  int32  \n",
      " 3   Car_Title        10065 non-null  object \n",
      " 4   Car_Year         10065 non-null  int32  \n",
      " 5   Car_Brand        10065 non-null  object \n",
      " 6   Car_Model        10065 non-null  object \n",
      " 7   Car_Location     10065 non-null  object \n",
      " 8   Car_Mileage(km)  10065 non-null  int32  \n",
      " 9   Car_Type         10065 non-null  object \n",
      " 10  Car_Gear         10065 non-null  object \n",
      " 11  Car_Seat         10065 non-null  float64\n",
      " 12  Car_Fuel(cc)     10065 non-null  int32  \n",
      " 13  Car_Door_Num     10065 non-null  float64\n",
      " 14  Car_Fuel_Type    10065 non-null  object \n",
      " 15  Car_Color        10065 non-null  object \n",
      "dtypes: float64(2), int32(5), object(9)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only keep valuable columns \n",
    "\n",
    "Car_data_target_final = Car_data_target_seat_door_fuel.drop(['Car_No', 'Car_Title', 'Car_Model', 'Car_Color'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9400</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>125384</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Price  Car_ORC  Car_Year   Car_Brand Car_Location  Car_Mileage(km)  \\\n",
       "0       3997        0      2005      Nissan      Dunedin           138150   \n",
       "1       7997        0      2009      Toyota      Dunedin           100300   \n",
       "2       9940        1      2007  Mitsubishi     Auckland           102425   \n",
       "3       8940        1      2006  Mitsubishi     Auckland           109425   \n",
       "5       9400        1      2010      Nissan     Auckland           125384   \n",
       "\n",
       "        Car_Type   Car_Gear  Car_Seat  Car_Fuel(cc)  Car_Door_Num  \\\n",
       "0      Hatchback  Automatic       5.0          1500           5.0   \n",
       "1  Station Wagon  Automatic       7.0          1800           5.0   \n",
       "2         RV/SUV  Automatic       5.0          2400           5.0   \n",
       "3         RV/SUV  Automatic       5.0          2400           5.0   \n",
       "5         RV/SUV  Automatic       5.0          2000           5.0   \n",
       "\n",
       "  Car_Fuel_Type  \n",
       "0        Petrol  \n",
       "1        Petrol  \n",
       "2        Petrol  \n",
       "3        Petrol  \n",
       "5        Petrol  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical features are:  ['Car_Brand', 'Car_Location', 'Car_Type', 'Car_Gear', 'Car_Fuel_Type']\n",
      "Numerical features are:  ['Car_Price', 'Car_ORC', 'Car_Year', 'Car_Mileage(km)', 'Car_Seat', 'Car_Fuel(cc)', 'Car_Door_Num']\n"
     ]
    }
   ],
   "source": [
    "# distinguish features into categorical and numerical features \n",
    "\n",
    "categorical_features = Car_data_target_final.select_dtypes(include = [\"object\"]).columns\n",
    "numerical_features = Car_data_target_final.select_dtypes(exclude = [\"object\"]).columns\n",
    "print('Categorical features are: ', categorical_features.tolist())\n",
    "print('Numerical features are: ',numerical_features.tolist())\n",
    "\n",
    "Car_data_categorical_columns = Car_data_target_final[categorical_features]\n",
    "\n",
    "Car_data_numerical_columns = Car_data_target_final[numerical_features].drop(['Car_Price'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dummy values \n",
    "\n",
    "df_categorical_columns_dummies = pd.get_dummies(Car_data_categorical_columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand_Aston</th>\n",
       "      <th>Car_Brand_Audi</th>\n",
       "      <th>Car_Brand_BMW</th>\n",
       "      <th>Car_Brand_Bentley</th>\n",
       "      <th>Car_Brand_Cadillac</th>\n",
       "      <th>Car_Brand_Chevrolet</th>\n",
       "      <th>Car_Brand_Chrysler</th>\n",
       "      <th>Car_Brand_Citroen</th>\n",
       "      <th>Car_Brand_Daihatsu</th>\n",
       "      <th>Car_Brand_Fiat</th>\n",
       "      <th>...</th>\n",
       "      <th>Car_Type_Ute</th>\n",
       "      <th>Car_Type_Van</th>\n",
       "      <th>Car_Gear_Automatic</th>\n",
       "      <th>Car_Gear_Manual</th>\n",
       "      <th>Car_Gear_Tiptronic</th>\n",
       "      <th>Car_Fuel_Type_Diesel</th>\n",
       "      <th>Car_Fuel_Type_Electric</th>\n",
       "      <th>Car_Fuel_Type_Hybrid</th>\n",
       "      <th>Car_Fuel_Type_LPG</th>\n",
       "      <th>Car_Fuel_Type_Petrol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Brand_Aston  Car_Brand_Audi  Car_Brand_BMW  Car_Brand_Bentley  \\\n",
       "0                0               0              0                  0   \n",
       "1                0               0              0                  0   \n",
       "2                0               0              0                  0   \n",
       "3                0               0              0                  0   \n",
       "5                0               0              0                  0   \n",
       "\n",
       "   Car_Brand_Cadillac  Car_Brand_Chevrolet  Car_Brand_Chrysler  \\\n",
       "0                   0                    0                   0   \n",
       "1                   0                    0                   0   \n",
       "2                   0                    0                   0   \n",
       "3                   0                    0                   0   \n",
       "5                   0                    0                   0   \n",
       "\n",
       "   Car_Brand_Citroen  Car_Brand_Daihatsu  Car_Brand_Fiat  ...  Car_Type_Ute  \\\n",
       "0                  0                   0               0  ...             0   \n",
       "1                  0                   0               0  ...             0   \n",
       "2                  0                   0               0  ...             0   \n",
       "3                  0                   0               0  ...             0   \n",
       "5                  0                   0               0  ...             0   \n",
       "\n",
       "   Car_Type_Van  Car_Gear_Automatic  Car_Gear_Manual  Car_Gear_Tiptronic  \\\n",
       "0             0                   1                0                   0   \n",
       "1             0                   1                0                   0   \n",
       "2             0                   1                0                   0   \n",
       "3             0                   1                0                   0   \n",
       "5             0                   1                0                   0   \n",
       "\n",
       "   Car_Fuel_Type_Diesel  Car_Fuel_Type_Electric  Car_Fuel_Type_Hybrid  \\\n",
       "0                     0                       0                     0   \n",
       "1                     0                       0                     0   \n",
       "2                     0                       0                     0   \n",
       "3                     0                       0                     0   \n",
       "5                     0                       0                     0   \n",
       "\n",
       "   Car_Fuel_Type_LPG  Car_Fuel_Type_Petrol  \n",
       "0                  0                     1  \n",
       "1                  0                     1  \n",
       "2                  0                     1  \n",
       "3                  0                     1  \n",
       "5                  0                     1  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_categorical_columns_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train : (7045, 70)\n",
      "X_test : (3020, 70)\n",
      "y_train : (7045,)\n",
      "y_test : (3020,)\n"
     ]
    }
   ],
   "source": [
    "# Create dataset for ML \n",
    "# Split dataset for the training and testing \n",
    "car_train_len = int(len(Car_data_target_final)*0.7) \n",
    "\n",
    "y_train = Car_data_target_final['Car_Price'][:car_train_len]\n",
    "\n",
    "y_test = Car_data_target_final['Car_Price'][car_train_len:]\n",
    "\n",
    "\n",
    "df_train_num = Car_data_numerical_columns[:car_train_len] \n",
    "df_train_dum = df_categorical_columns_dummies[:car_train_len]\n",
    "X_train = pd.concat([df_train_num, df_train_dum], axis = 1)\n",
    "df_train = pd.concat([X_train, y_train], axis = 1)\n",
    "\n",
    "\n",
    "df_test_num = Car_data_numerical_columns[car_train_len:] \n",
    "df_test_dum = df_categorical_columns_dummies[car_train_len:]\n",
    "X_test = pd.concat([df_test_num, df_test_dum], axis = 1)\n",
    "df_test = pd.concat([X_test, y_test], axis = 1)\n",
    "\n",
    "\n",
    "print(\"X_train : \" + str(X_train.shape))\n",
    "print(\"X_test : \" + str(X_test.shape))\n",
    "print(\"y_train : \" + str(y_train.shape))\n",
    "print(\"y_test : \" + str(y_test.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Price                1.000000\n",
       "Car_Fuel(cc)             0.417299\n",
       "Car_Year                 0.381062\n",
       "Car_Fuel_Type_Diesel     0.367488\n",
       "Car_Brand_Lamborghini    0.363655\n",
       "                           ...   \n",
       "Car_Brand_Smart          0.001320\n",
       "Car_Brand_Cadillac       0.001038\n",
       "Car_Type_Sedan           0.001023\n",
       "Car_Brand_Renault             NaN\n",
       "Car_Brand_Rolls-Royce         NaN\n",
       "Name: Car_Price, Length: 71, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train_corr = pd.concat([X_train, y_train], axis = 1)\n",
    "corr_value = df_train_corr.corr().abs()\n",
    "sns.heatmap(corr_value,square=True,vmax=1,vmin=-1,center=0.0,cmap='coolwarm')\n",
    "corr_value.sort_values([\"Car_Price\"], ascending = False, inplace = True)\n",
    "corr_value[\"Car_Price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Naive Bayes Model\n",
    "nb_model = GaussianNB()\n",
    "nb_model.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7995,  6900,  7995, ..., 14499, 14250,  7995])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predict modl\n",
    "nb_y_pred = nb_model.predict(X_test)\n",
    "nb_y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6490066225165565"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy Score\n",
    "nb_accuracy = accuracy_score(y_test,nb_y_pred) * 100\n",
    "nb_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42634388.896688744"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_mse = mean_squared_error(y_test, nb_y_pred)\n",
    "nb_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4900</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7400</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8900</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9900</td>\n",
       "      <td>12390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5800</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3015</th>\n",
       "      <td>14990</td>\n",
       "      <td>10333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3016</th>\n",
       "      <td>7995</td>\n",
       "      <td>6590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3017</th>\n",
       "      <td>14450</td>\n",
       "      <td>14499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3018</th>\n",
       "      <td>12800</td>\n",
       "      <td>14250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>4999</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3020 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       test predict\n",
       "0      4900    7995\n",
       "1      7400    6900\n",
       "2      8900    7995\n",
       "3      9900   12390\n",
       "4      5800    7995\n",
       "...     ...     ...\n",
       "3015  14990   10333\n",
       "3016   7995    6590\n",
       "3017  14450   14499\n",
       "3018  12800   14250\n",
       "3019   4999    7995\n",
       "\n",
       "[3020 rows x 2 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compare and visualize the test and predict sample\n",
    "nb_compare_plot = pd.DataFrame({},columns = ['test','predict'])\n",
    "nb_compare_plot\n",
    "\n",
    "for i in range(len(y_test)):\n",
    "    nb_compare_plot.loc[i] = [y_test.values[i],nb_y_pred[i]]\n",
    "    \n",
    "nb_compare_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_compare_plot.plot(title = 'Naive Bayes test VS predict', figsize=(15,8))\n",
    "plt.xlabel('ID')\n",
    "plt.ylabel('Car Price')\n",
    "plt.grid(True)\n",
    "plt.grid(alpha = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4900</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7400</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8900</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9900</td>\n",
       "      <td>12390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5800</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>9950</td>\n",
       "      <td>9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>12990</td>\n",
       "      <td>11050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>8990</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>12990</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>9990</td>\n",
       "      <td>7995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      test predict\n",
       "0     4900    7995\n",
       "1     7400    6900\n",
       "2     8900    7995\n",
       "3     9900   12390\n",
       "4     5800    7995\n",
       "..     ...     ...\n",
       "496   9950    9995\n",
       "497  12990   11050\n",
       "498   8990    7995\n",
       "499  12990    7995\n",
       "500   9990    7995\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Small Scaler version of compare\n",
    "\n",
    "nb_compare_plot = pd.DataFrame({},columns = ['test','predict'])\n",
    "nb_compare_plot\n",
    "\n",
    "for i in range(len(y_test)):\n",
    "    if(y_test.values[i] > 150000):\n",
    "        continue;\n",
    "    elif (i > 500):\n",
    "        break;\n",
    "    \n",
    "    nb_compare_plot.loc[i] = [y_test.values[i],nb_y_pred[i]]\n",
    "    \n",
    "nb_compare_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_compare_plot.plot(title = 'Naive Bayes test VS predict', figsize=(15,8))\n",
    "plt.xlabel('ID')\n",
    "plt.ylabel('Car Price')\n",
    "plt.grid(True)\n",
    "plt.grid(alpha = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3015</th>\n",
       "      <td>4657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3016</th>\n",
       "      <td>1405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3017</th>\n",
       "      <td>-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3018</th>\n",
       "      <td>-1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>-2996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3020 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       diff\n",
       "0     -3095\n",
       "1       500\n",
       "2       905\n",
       "3     -2490\n",
       "4     -2195\n",
       "...     ...\n",
       "3015   4657\n",
       "3016   1405\n",
       "3017    -49\n",
       "3018  -1450\n",
       "3019  -2996\n",
       "\n",
       "[3020 rows x 1 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Some analysie with the difference \n",
    "nb_difference_plot = pd.DataFrame({},columns = ['diff'])\n",
    "\n",
    "for i in range(len(y_test)):\n",
    "    nb_difference_plot.loc[i] = y_test.values[i] - nb_y_pred[i]\n",
    "                                                \n",
    "nb_difference_plot                                             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_difference_plot.plot(title = 'Naive Bayes test VS predict difference', figsize=(15,8))\n",
    "plt.xlabel('ID')\n",
    "plt.ylabel('Car Price Difference')\n",
    "plt.grid(True)\n",
    "plt.grid(alpha = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3020.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>211.33245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14110.09777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-82095.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-2300.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2660.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>289020.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               diff\n",
       "count    3020.00000\n",
       "mean      211.33245\n",
       "std     14110.09777\n",
       "min    -82095.00000\n",
       "25%     -2300.00000\n",
       "50%         0.00000\n",
       "75%      2660.50000\n",
       "max    289020.00000"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_difference_plot['diff'] =nb_difference_plot['diff'].astype('int')\n",
    "nb_difference_plot.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean is about 211, it's the value really close to 0. Looks the predict only have small range of error.\n",
    "\n",
    "However, it's maybe due to the positive and negative value are balance each other. So, we make every value positive and see what the result is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3020.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5598.530464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12953.208477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2495.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>289020.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                diff\n",
       "count    3020.000000\n",
       "mean     5598.530464\n",
       "std     12953.208477\n",
       "min         0.000000\n",
       "25%      1000.000000\n",
       "50%      2495.000000\n",
       "75%      5400.000000\n",
       "max    289020.000000"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_abs_difference_plot = nb_difference_plot.copy()\n",
    "for i in range(len(nb_abs_difference_plot)):\n",
    "    if(nb_abs_difference_plot.loc[i].values < 0):\n",
    "        nb_abs_difference_plot.loc[i] *= -1;\n",
    "        \n",
    "nb_abs_difference_plot.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_abs_difference_plot.plot(title = 'Naive Bayes test VS predict absolute difference', figsize=(15,8))\n",
    "plt.xlabel('ID')\n",
    "plt.ylabel('Car Price Difference')\n",
    "plt.grid(True)\n",
    "plt.grid(alpha = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.23% datas difference small than 500\n",
      "21.89% datas difference small than 1000\n",
      "73.21% datas difference small than 5000\n"
     ]
    }
   ],
   "source": [
    "less_500 = 0\n",
    "less_1000 = 0\n",
    "less_5000 = 0\n",
    "\n",
    "for i in range(len(nb_abs_difference_plot)):\n",
    "    if(nb_abs_difference_plot.loc[i].values < 5000):\n",
    "        less_5000 += 1;\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "    if(nb_abs_difference_plot.loc[i].values < 1000):\n",
    "        less_1000 += 1;\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "    if(nb_abs_difference_plot.loc[i].values < 500):\n",
    "        less_500 += 1;\n",
    "    \n",
    "print(\"{:.2f}\".format(less_500 / len(nb_abs_difference_plot) * 100) + \"% datas difference small than 500\")\n",
    "print(\"{:.2f}\".format(less_1000 / len(nb_abs_difference_plot) * 100) + \"% datas difference small than 1000\")\n",
    "print(\"{:.2f}\".format(less_5000 / len(nb_abs_difference_plot) * 100) + \"% datas difference small than 5000\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naive Bayes is not that accurate with this data set."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
